Paper Digest: WWW 2024 Papers & Highlights
The Web Conference (WWW) is one of the top internet conferences in the world. To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.
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TABLE 1: Paper Digest: WWW 2024 Papers & Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | ClickPrompt: CTR Models Are Strong Prompt Generators for Adapting Language Models to CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To benefit from both worlds and close their gaps, we propose a novel model-agnostic framework (i.e., ClickPrompt), where we incorporate CTR models to generate interaction-aware soft prompts for PLMs. |
Jianghao Lin; Bo Chen; Hangyu Wang; Yunjia Xi; Yanru Qu; Xinyi Dai; Kangning Zhang; Ruiming Tang; Yong Yu; Weinan Zhang; |
| 2 | ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. |
Jianghao Lin; Rong Shan; Chenxu Zhu; Kounianhua Du; Bo Chen; Shigang Quan; Ruiming Tang; Yong Yu; Weinan Zhang; |
| 3 | AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Human non-verbal behaviors, such as item clicking in recommender systems, although implicitly exhibiting user preferences and could enhance the modeling of users, have not been deeply explored. The main reasons lie in the gap between language modeling and behavior modeling, as well as the incomprehension of LLMs about user-item relations.To address this issue, we propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. |
Junjie Zhang; Yupeng Hou; Ruobing Xie; Wenqi Sun; Julian McAuley; Wayne Xin Zhao; Leyu Lin; Ji-Rong Wen; |
| 4 | Graph Out-of-Distribution Generalization Via Causal Intervention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we adopt a bottom-up data-generative perspective and reveal a key observation through causal analysis: the crux of GNNs’ failure in OOD generalization lies in the latent confounding bias from the environment. |
Qitian Wu; Fan Nie; Chenxiao Yang; Tianyi Bao; Junchi Yan; |
| 5 | SPRING: Improving The Throughput of Sharding Blockchain Via Deep Reinforcement Learning Based State Placement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present SPRING, the first deep-reinforcement-learning(DRL)-based sharding framework for state placement. |
Pengze Li; Mingxuan Song; Mingzhe Xing; Zhen Xiao; Qiuyu Ding; Shengjie Guan; Jieyi Long; |
| 6 | Representation Learning with Large Language Models for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While the integration of large language models (LLMs) into traditional ID-based recommenders has gained attention, challenges such as scalability issues, limitations in text-only reliance, and prompt input constraints need to be addressed for effective implementation in practical recommender systems. To address these challenges, we propose a model-agnostic framework RLMRec that aims to enhance existing recommenders with LLM-empowered representation learning. |
Xubin Ren; Wei Wei; Lianghao Xia; Lixin Su; Suqi Cheng; Junfeng Wang; Dawei Yin; Chao Huang; |
| 7 | Off-Policy Evaluation for Large Action Spaces Via Policy Convolution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, importance sampling relies on the common support assumption, which becomes impractical when the action space is large. To address these challenges, we introduce the Policy Convolution (PC) family of estimators for the contextual bandit setting. |
Noveen Sachdeva; Lequn Wang; Dawen Liang; Nathan Kallus; Julian McAuley; |
| 8 | Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Among them is web search, where knowing what a user is trying to accomplish, what they care about, and what they know can lead to improved search experiences. In this work, we propose a novel and general approach that augments an LLM with relevant context from users’ interaction histories with a search engine in order to personalize its outputs. |
Jinheon Baek; Nirupama Chandrasekaran; Silviu Cucerzan; Allen Herring; Sujay Kumar Jauhar; |
| 9 | Getting Bored of Cyberwar: Exploring The Role of Low-level Cybercrime Actors in The Russia-Ukraine Conflict Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We analyse 358k website defacement attacks, 1.7M UDP amplification DDoS attacks, 1764 posts made by 372 users on Hack Forums mentioning the two countries, and 441 Telegram announcements (with 58k replies) of a volunteer hacking group for two months before and four months after the invasion. |
Anh V. Vu; Daniel R. Thomas; Ben Collier; Alice Hutchings; Richard Clayton; Ross Anderson; |
| 10 | Unmasking The Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We first propose a framework based on node pruning that emerges superior, particularly when combining multiple behavioral indicators across different networks. Then, we introduce a supervised machine learning model that harnesses a vector representation of the fused similarity network. |
Luca Luceri; Valeria Pant\`{e}; Keith Burghardt; Emilio Ferrara; |
| 11 | Scalable and Effective Generative Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In more detail, we propose RIPOR- an optimization framework for generative retrieval that is designed based on two often-overlooked fundamental design considerations. |
Hansi Zeng; Chen Luo; Bowen Jin; Sheikh Muhammad Sarwar; Tianxin Wei; Hamed Zamani; |
| 12 | Cost-effective Data Labelling for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an unsupervised, scalable and flexible AL method – it incurs low memory footprints and time cost, is flexible to the choice of underlying GNNs, and operates without requiring GNN-model-specific knowledge or labels of selected nodes. |
Shixun Huang; Ge Lee; Zhifeng Bao; Shirui Pan; |
| 13 | KGQuiz: Evaluating The Generalization of Encoded Knowledge in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose KGQuiz, a knowledge-intensive benchmark to comprehensively investigate the knowledge generalization abilities of LLMs. |
Yuyang Bai; Shangbin Feng; Vidhisha Balachandran; Zhaoxuan Tan; Shiqi Lou; Tianxing He; Yulia Tsvetkov; |
| 14 | EXGC: Bridging Efficiency and Explainability in Graph Condensation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, in this work, we pinpoint two major inefficiencies of current paradigms: (1) the concurrent updating of a vast parameter set, and (2) pronounced parameter redundancy. |
Junfeng Fang; Xinglin Li; Yongduo Sui; Yuan Gao; Guibin Zhang; Kun Wang; Xiang Wang; Xiangnan He; |
| 15 | MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Domain-specific finetuning is an effective solution, but faces two critical challenges: 1) lack of high-quality training data. 2) no open-source foundation LLMs. To alleviate these problems, we formally model interpretable mental health analysis as a text generation task, and build the first multi-task and multi-source interpretable mental health instruction (IMHI) dataset with 105K data samples to support LLM instruction tuning and evaluation. |
Kailai Yang; Tianlin Zhang; Ziyan Kuang; Qianqian Xie; Jimin Huang; Sophia Ananiadou; |
| 16 | Globally Interpretable Graph Learning Via Distribution Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To gain global insights, we aim to answer an important question that is not yet well studied: how to provide a global interpretation for the graph learning procedure? |
Yi Nian; Yurui Chang; Wei Jin; Lu Lin; |
| 17 | Back to The Future: Towards Explainable Temporal Reasoning with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce the first task of explainable temporal reasoning, to predict an event’s occurrence at a future timestamp based on context which requires multiple reasoning over multiple events, and subsequently provide a clear explanation for their prediction. |
Chenhan Yuan; Qianqian Xie; Jimin Huang; Sophia Ananiadou; |
| 18 | Learning to Generate Explainable Stock Predictions Using Self-Reflective Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle these issues, we propose our Summarize-Explain-Predict (SEP) framework, which utilizes a verbal self-reflective agent and Proximal Policy Optimization (PPO) that allow a LLM teach itself how to generate explainable stock predictions, in a fully autonomous manner. |
Kelvin J.L. Koa; Yunshan Ma; Ritchie Ng; Tat-Seng Chua; |
| 19 | HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To mitigate the impact of false negative triples during the generation of hard negative triples, we propose the Hardness and Structure-aware (HaSa) contrastive KGE method, which alleviates the effect of false negative triples while generating the hard negative triples. |
Honggen Zhang; June Zhang; Igor Molybog; |
| 20 | Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper provides a framework to investigate the effectiveness of LLMs as multi-lingual dialogue systems for healthcare queries. |
Yiqiao Jin; Mohit Chandra; Gaurav Verma; Yibo Hu; Munmun De Choudhury; Srijan Kumar; |
| 21 | Unity Is Strength? Benchmarking The Robustness of Fusion-based 3D Object Detection Against Physical Sensor Attack Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on investigating MSF security under various sensor attacks and wish to answer the following research questions: (1)Does fusion enhance robustness or not? |
Zizhi Jin; Xuancun Lu; Bo Yang; Yushi Cheng; Chen Yan; Xiaoyu Ji; Wenyuan Xu; |
| 22 | Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Zero-shot Document-level Relation Triplet Extraction (ZeroDocRTE) framework, which Generates labeled data by Retrieval and Denoising Knowledge from LLMs, called GenRDK. |
Qi Sun; Kun Huang; Xiaocui Yang; Rong Tong; Kun Zhang; Soujanya Poria; |
| 23 | InfoRank: Unbiased Learning-to-Rank Via Conditional Mutual Information Minimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple yet sufficient unbiased learning-to-rank paradigm named InfoRank that aims to simultaneously address both position and popularity biases. |
Jiarui Jin; Zexue He; Mengyue Yang; Weinan Zhang; Yong Yu; Jun Wang; Julian McAuley; |
| 24 | Item-side Fairness of Large Language Model-based Recommendation System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To bridge this gap, this study examines the property of LRS with respect to item-side fairness and reveals the influencing factors of both historical users’ interactions and inherent semantic biases of LLMs, shedding light on the need to extend conventional item-side fairness methods for LRS. Towards this goal, we develop a concise and effective framework called IFairLRS to enhance the item-side fairness of an LRS. |
Meng Jiang; Keqin Bao; Jizhi Zhang; Wenjie Wang; Zhengyi Yang; Fuli Feng; Xiangnan He; |
| 25 | Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a novel framework named Search-in-the-Chain (SearChain) for the interaction between LLM and IR to solve the challenges. |
Shicheng Xu; Liang Pang; Huawei Shen; Xueqi Cheng; Tat-Seng Chua; |
| 26 | MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, in this paper, we propose MultiGPrompt, a novel multi-task pre-training and prompting framework to exploit multiple pretext tasks for more comprehensive pre-trained knowledge. |
Xingtong Yu; Chang Zhou; Yuan Fang; Xinming Zhang; |
| 27 | Blockchain Censorship Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formalize, quantify, and analyze the security impact of blockchain censorship. |
Anton Wahrst\{a}tter; Jens Ernstberger; Aviv Yaish; Liyi Zhou; Kaihua Qin; Taro Tsuchiya; Sebastian Steinhorst; Davor Svetinovic; Nicolas Christin; Mikolaj Barczentewicz; Arthur Gervais; |
| 28 | Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce an empirical framework to investigate users’ susceptibility to influence when exposed to unreliable and reliable information sources. |
Jinyi Ye; Luca Luceri; Julie Jiang; Emilio Ferrara; |
| 29 | NAT4AT: Using Non-Autoregressive Translation Makes Autoregressive Translation Faster and Better Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, in this paper, we first conducted analysis experiments at the sentence level and found complementarity and high similarity between the translations generated by AT and NAT. Then, based on this observation, we propose a general and effective method called NAT4AT, which can not only use NAT to speed up the inference speed of AT significantly but also improve its final translation quality. |
Huanran Zheng; Wei Zhu; Xiaoling Wang; |
| 30 | High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text Attributed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, existing GNNs rarely model text in each node in a contextualized way; existing PLMs can hardly be applied to characterize graph structures due to their sequence architecture. To address these challenges, we propose HASH-CODE, a High-frequency Aware Spectral Hierarchical Contrastive Selective Coding method that integrates GNNs and PLMs into a unified model. |
Peiyan Zhang; Chaozhuo Li; Liying Kang; Feiran Huang; Senzhang Wang; Xing Xie; Sunghun Kim; |
| 31 | Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Alternatively, the more efficient accuracy and AUC metrics often fall short of capturing the true targets of recommendation tasks, leading to suboptimal performance. To overcome this dilemma, we propose a new optimization metric, Lower-Left Partial AUC (LLPAUC), which is computationally efficient like AUC but strongly correlates with Top-K ranking metrics. |
Wentao Shi; Chenxu Wang; Fuli Feng; Yang Zhang; Wenjie Wang; Junkang Wu; Xiangnan He; |
| 32 | An In-depth Investigation of User Response Simulation for Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new generation system to effectively cover the training blind spot and suggest a new evaluation setup to avoid misevaluation. |
Zhenduo Wang; Zhichao Xu; Vivek Srikumar; Qingyao Ai; |
| 33 | LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we propose a Light Anti-overfitting Retraining Approach (LARA) based on deep variational auto-encoders for time series anomaly detection. |
Feiyi Chen; Zhen Qin; Mengchu Zhou; Yingying Zhang; Shuiguang Deng; Lunting Fan; Guansong Pang; Qingsong Wen; |
| 34 | Uplift Modeling for Target User Attacks on Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recommender systems are vulnerable to injective attacks, which inject limited fake users into the platforms to manipulate the exposure of target items to all users. In this work, we identify that conventional injective attackers overlook the fact that each item has its unique potential audience, and meanwhile, the attack difficulty across different users varies. |
Wenjie Wang; Changsheng Wang; Fuli Feng; Wentao Shi; Daizong Ding; Tat-Seng Chua; |
| 35 | Could Small Language Models Serve As Recommenders? Towards Data-centric Cold-start Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the problem, we propose PromptRec, a simple but effective approach based on in-context learning of language models, where we transform the recommendation task into the sentiment analysis task on natural language containing user and item profiles. |
Xuansheng Wu; Huachi Zhou; Yucheng Shi; Wenlin Yao; Xiao Huang; Ninghao Liu; |
| 36 | Whole Page Unbiased Learning to Rank Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, those biases widely exist in industrial systems and may lead to an unsatisfactory search experience. Therefore, we introduce a new problem, i.e., whole-page Unbiased Learning to Rank(WP-ULTR), aiming to handle biases induced by whole-page SERP features simultaneously. |
Haitao Mao; Lixin Zou; Yujia Zheng; Jiliang Tang; Xiaokai Chu; Jiashu Zhao; Qian Wang; Dawei Yin; |
| 37 | Unifying Local and Global Knowledge: Empowering Large Language Models As Political Experts with Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, the nature of political questions often renders the direct facts elusive, necessitating deeper aggregation and comprehension of retrieved evidence. To address these challenges, we propose a Political Experts through Knowledge Graph Integration (PEG) framework. |
Xinyi Mou; Zejun Li; Hanjia Lyu; Jiebo Luo; Zhongyu Wei; |
| 38 | Is Contrastive Learning Necessary? A Study of Data Augmentation Vs Contrastive Learning in Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This raises the question: Is it possible to achieve superior recommendation results solely through data augmentation? To answer this question, we benchmark eight widely used data augmentation strategies, as well as state-of-the-art CL-based SRS methods, on four real-world datasets under both warm- and cold-start settings. |
Peilin Zhou; You-Liang Huang; Yueqi Xie; Jingqi Gao; Shoujin Wang; Jae Boum Kim; Sunghun Kim; |
| 39 | Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the aforementioned issues, firstly, we develop the Efficient Behavior Sequence Miner (EBM) that efficiently captures intricate patterns in user behavior while maintaining low time complexity and parameter count. |
Yongqiang Han; Hao Wang; Kefan Wang; Likang Wu; Zhi Li; Wei Guo; Yong Liu; Defu Lian; Enhong Chen; |
| 40 | Rethinking Cross-Domain Sequential Recommendation Under Open-World Assumptions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, current CDSR methods make closed-world assumptions, assuming fully overlapping users across multiple domains and that the data distribution remains unchanged from the training environment to the test environment. As a result, these methods typically result in lower performance on online real-world platforms due to the data distribution shifts. To address these challenges under open-world assumptions, we design an Adaptive Multi-Interest Debiasing framework for cross-domain sequential recommendation (AMID), which consists of a multi-interest information module (MIM) and a doubly robust estimator (DRE). |
Wujiang Xu; Qitian Wu; Runzhong Wang; Mingming Ha; Qiongxu Ma; Linxun Chen; Bing Han; Junchi Yan; |
| 41 | Bidder Selection Problem in Position Auctions: A Fast and Simple Algorithm Via Poisson Approximation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel Poisson relaxation of BSP for position auctions that immediately implies that 1) BSP is polynomial-time solvable up to a vanishingly small error as the problem size k grows; 2) there is a PTAS for position auctions after combining our relaxation with the trivial brute force algorithm. |
Nikolai Gravin; Yixuan Even Xu; Renfei Zhou; |
| 42 | Mitigating Exploitation Bias in Learning to Rank with An Uncertainty-aware Empirical Bayes Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Because user behavior signals often have strong correlations with the ranking objective and can only be collected on items that have already been shown to users, directly using behavior signals in LTR could create an exploitation bias that hurts the system performance in the long run.To address the exploitation bias, we propose an uncertainty-aware empirical Bayes based ranking algorithm, referred to as EBRank. |
Tao Yang; Cuize Han; Chen Luo; Parth Gupta; Jeff M. Phillips; Qingyao Ai; |
| 43 | Learning Category Trees for ID-Based Recommendation: Exploring The Power of Differentiable Vector Quantization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel approach to automatically learn and generate entity (i.e., user or item) category trees for ID-based recommendation. |
Qijiong Liu; Jiaren Xiao; Lu Fan; Jieming Zhu; Xiao-Ming Wu; |
| 44 | Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper presents a cloud-device collaborative graph neural network federated recommendation model, named CDCGNNFed. |
Liang Qu; Wei Yuan; Ruiqi Zheng; Lizhen Cui; Yuhui Shi; Hongzhi Yin; |
| 45 | Collaborative Large Language Model for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the semantic gap between natural language and recommendation tasks is still not well addressed, leading to multiple issues such as spuriously correlated user/item descriptors, ineffective language modeling on user/item data, inefficient recommendations via auto-regression, etc. In this paper, we propose CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and ID paradigm of RSs, aiming to address the above challenges simultaneously. |
Yaochen Zhu; Liang Wu; Qi Guo; Liangjie Hong; Jundong Li; |
| 46 | Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, Local Differential Privacy (LDP), the traditional privacy-preserving method for CL frameworks, has also been proven ineffective against PTIA. In light of this, we propose a novel defense mechanism (AGD) against PTIA based on an adversarial game to eliminate sensitive POIs and their information in correlated POIs. |
Jing Long; Tong Chen; Guanhua Ye; Kai Zheng; Quoc Viet Hung Nguyen; Hongzhi Yin; |
| 47 | Learning to Rewrite Prompts for Personalized Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method to automatically revise prompts for personalized text generation. |
Cheng Li; Mingyang Zhang; Qiaozhu Mei; Weize Kong; Michael Bendersky; |
| 48 | Learning Scalable Structural Representations for Link Prediction with Bloom Signatures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent works resort to learning more expressive edge-wise representations by enhancing vanilla GNNs with structural features such as labeling tricks and link prediction heuristics, but they suffer from high computational overhead and limited scalability. To tackle this issue, we propose to learn structural link representations by augmenting the message-passing framework of GNNs with Bloom signatures. |
Tianyi Zhang; Haoteng Yin; Rongzhe Wei; Pan Li; Anshumali Shrivastava; |
| 49 | A Knowledge-Injected Curriculum Pretraining Framework for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a general K nowledge-I njected C urriculum P retraining framework (KICP) to achieve comprehensive KG learning and exploitation for KBQA tasks, which is composed of knowledge injection (KI), knowledge adaptation (KA) and curriculum reasoning (CR). |
Xin Lin; Tianhuang Su; Zhenya Huang; Shangzi Xue; Haifeng Liu; Enhong Chen; |
| 50 | Incentive and Dynamic Client Selection for Federated Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: At the lower level, we utilize evolutionary game theory to model the dynamic participation process, aiming to attract remaining clients to participate in retraining tasks. |
Yijing Lin; Zhipeng Gao; Hongyang Du; Dusit Niyato; Jiawen Kang; Xiaoyuan Liu; |
| 51 | Dual Box Embeddings for The Description Logic EL++ Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While a variety of approaches have been proposed, current ontology embedding methods suffer from several shortcomings, especially that they all fail to faithfully model one-to-many, many-to-one, and many-to-many relations and role inclusion axioms. To address this problem and improve ontology completion performance, we propose a novel ontology embedding method named Box2EL for the DL EL++, which represents both concepts and roles as boxes (i.e., axis-aligned hyperrectangles), and models inter-concept relationships using a bumping mechanism. |
Mathias Jackermeier; Jiaoyan Chen; Ian Horrocks; |
| 52 | User Response in Ad Auctions: An MDP Formulation of Long-term Revenue Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new Markov Decision Process (MDP) model for ad auctions to capture the user response to the quality of ads, with the objective of maximizing the long-term discounted revenue. |
Yang Cai; Zhe Feng; Christopher Liaw; Aranyak Mehta; Grigoris Velegkas; |
| 53 | Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To integrate the strength of both contrastive learning and meta learning on the few-shot node classification tasks, we introduce a new paradigm-Contrastive Few-Shot Node Classification (COLA). |
Hao Liu; Jiarui Feng; Lecheng Kong; Dacheng Tao; Yixin Chen; Muhan Zhang; |
| 54 | Improving Retrieval in Theme-specific Applications Using A Corpus Topical Taxonomy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To capture the theme-specific information and improve retrieval, we propose to use a corpus topical taxonomy, which outlines the latent topic structure of the corpus while reflecting user-interested aspects. |
SeongKu Kang; Shivam Agarwal; Bowen Jin; Dongha Lee; Hwanjo Yu; Jiawei Han; |
| 55 | Intelligent Model Update Strategy for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we argue that frequent data exchanges between the cloud and edges often lead to inefficiency and waste of communication/computation resources, as considerable parameter updates might be redundant. To investigate this problem, we introduce Intelligent Edge-Cloud Parameter Request Model (IntellectReq). |
Zheqi Lv; Wenqiao Zhang; Zhengyu Chen; Shengyu Zhang; Kun Kuang; |
| 56 | Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, we point out that it is inappropriate to indiscriminately apply PRP through every stage of a contemporary IR system. Such systems contain multiple stages (e.g., retrieval, pre-ranking, ranking, and re-ranking stages, as examined in this paper). |
Kai Zheng; Haijun Zhao; Rui Huang; Beichuan Zhang; Na Mou; Yanan Niu; Yang Song; Hongning Wang; Kun Gai; |
| 57 | Towards The Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners’ cognitive states and the quality of web learning services. To address these problems, we propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novelresponse-proficiency-response paradigm inspired by encoder-decoder models. |
Jiatong Li; Qi Liu; Fei Wang; Jiayu Liu; Zhenya Huang; Fangzhou Yao; Linbo Zhu; Yu Su; |
| 58 | IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Integrating Multi-curvature shared and specific Embedding (IME) model for TKGC tasks. |
Jiapu Wang; Zheng Cui; Boyue Wang; Shirui Pan; Junbin Gao; Baocai Yin; Wen Gao; |
| 59 | Recommender Transformers with Behavior Pathways Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we build the Recommender Transformer (RETR) with a novel Pathway Attention mechanism. |
Zhiyu Yao; Xinyang Chen; Sinan Wang; Qinyan Dai; Yumeng Li; Tanchao Zhu; Mingsheng Long; |
| 60 | Towards Energy-efficient Federated Learning Via INT8-based Training on Mobile DSPs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, we observe that there are still unavoidable frequent quantization operations on devices that cause extreme load stress on DSP-enabled INT8 training. To address the above challenges, we present Q-FedUpdate, an FL framework that efficiently preserves model accuracy with ultra-low energy consumption. |
Jinliang Yuan; Shangguang Wang; Hongyu Li; Daliang Xu; Yuanchun Li; Mengwei Xu; Xuanzhe Liu; |
| 61 | Ensuring User-side Fairness in Dynamic Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Guided by our theoretical analyses, we propose FAir Dynamic rEcommender (FADE), an end-to-end fine-tuning framework to dynamically ensure user-side fairness over time. |
Hyunsik Yoo; Zhichen Zeng; Jian Kang; Ruizhong Qiu; David Zhou; Zhining Liu; Fei Wang; Charlie Xu; Eunice Chan; Hanghang Tong; |
| 62 | Enhancing Recommendation Accuracy and Diversity with Box Embedding: A Universal Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose LCD-UC , a novel List-Check-Decide framework with UnCertainty masking based on box embedding to improve recommendation diversity with recommendation accuracy maintained. |
Cheng Wu; Shaoyun Shi; Chaokun Wang; Ziyang Liu; Wang Peng; Wenjin Wu; Dongying Kong; Han Li; Kun Gai; |
| 63 | Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, using the same reference data for all collaborations can impede knowledge exchange and lead to sub-optimal performance. To address this gap, we introduce the Decentralized Collaborative Learning with Adaptive Reference Data (DARD) framework, which crafts adaptive reference data for effective user collaboration. |
Ruiqi Zheng; Liang Qu; Tong Chen; Lizhen Cui; Yuhui Shi; Hongzhi Yin; |
| 64 | RecDCL: Dual Contrastive Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we investigate how to employ both batch-wise CL (BCL) and feature-wise CL (FCL) for recommendation. |
Dan Zhang; Yangliao Geng; Wenwen Gong; Zhongang Qi; Zhiyu Chen; Xing Tang; Ying Shan; Yuxiao Dong; Jie Tang; |
| 65 | Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Though several studies have conducted privacy preserving CDR via Federated Learning (FL), they still have the following limitations: 1) They need to upload users’ personal information to the central server, posing the risk of leaking user privacy. 2) Existing federated methods mainly rely on atomic item IDs to represent items, which prevents them from modeling items in a unified feature space, increasing the challenge of knowledge transfer among domains. 3) They are all based on the premise of knowing overlapped users between domains, which proves impractical in real-world applications. To address the above limitations, we focus on Privacy-preserving Cross-domain Recommendation (PCDR) and propose PFCR as our solution. |
Lei Guo; Ziang Lu; Junliang Yu; Quoc Viet Hung Nguyen; Hongzhi Yin; |
| 66 | FedDSE: Distribution-aware Sub-model Extraction for Federated Learning Over Resource-constrained Devices Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: If highly activated neurons from some clients with one distribution are incorporated into the sub-model allocated to other clients with different distributions, they will be forced to fit the new distributions, which can hinder their activation over the previous clients and result in a performance reduction. Motivated by this finding, we propose a novel method called FedDSE, which can reduce the conflicts among clients by extracting sub-models based on the data distribution of each client. |
Haozhao Wang; Yabo Jia; Meng Zhang; Qinghao Hu; Hao Ren; Peng Sun; Yonggang Wen; Tianwei Zhang; |
| 67 | Robust Link Prediction Over Noisy Hyper-Relational Knowledge Graphs Via Active Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, hyper-relational facts, where an arbitrary number of key-value pairs are associated with a base triplet, have become increasingly popular in modern KGs, but significantly complicate the confidence assessment of the fact. Against this background, we study the problem of robust link prediction over noisy hyper-relational KGs, and propose NYLON, a underlineN oise-resistant hunderlineY per-reunderlineL atiunderlineON al link prediction technique via active crowd learning. |
Weijian Yu; Jie Yang; Dingqi Yang; |
| 68 | Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and model capabilities; and the existing metric-driven methods such as the Lambda framework can only optimize a rough upper bound of limited metrics, potentially resulting in sub-optimal and performance misalignment. To address these issues, we propose a novel perspective on optimizing cascade ranking systems by highlighting the adaptability of optimization targets to data complexities and model capabilities. |
Yunli Wang; Zhiqiang Wang; Jian Yang; Shiyang Wen; Dongying Kong; Han Li; Kun Gai; |
| 69 | From Promises to Practice: Evaluating The Private Browsing Modes of Android Browser Apps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge the gap, in this work, we systemically studied the private browsing modes of Android browser apps. |
Xiaoyin Liu; Wenzhi Li; Qinsheng Hou; Shishuai Yang; Lingyun Ying; Wenrui Diao; Yanan Li; Shanqing Guo; Haixin Duan; |
| 70 | Challenging Low Homophily in Social Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To comprehensively extract preference-aware homophily information latent in the social graph, we propose Social Heterophily-alleviating Rewiring (SHaRe), a data-centric framework for enhancing existing graph-based social recommendation models. |
Wei Jiang; Xinyi Gao; Guandong Xu; Tong Chen; Hongzhi Yin; |
| 71 | Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the efficiency of non-truthful auctions for auto-bidders with both return on spend (ROS) and budget constraints. |
Christopher Liaw; Aranyak Mehta; Wennan Zhu; |
| 72 | Can GNN Be Good Adapter for LLMs? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Additionally, they also ignore the zero-shot inference capabilities of LLMs. Therefore, we propose GraphAdapter, which uses a graph neural network (GNN) as an efficient adapter in collaboration with LLMs to tackle TAGs. |
Xuanwen Huang; Kaiqiao Han; Yang Yang; Dezheng Bao; Quanjin Tao; Ziwei Chai; Qi Zhu; |
| 73 | Challenges Toward AGI and Its Impact to The Web Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the second part of the talk, we will use ChatGLM, an alternative but open sourced model to ChatGPT, as an example to explain our understandings and insights derived during the implementation of the model. |
Bo Zhang; Jie Tang; |
| 74 | Modeling The Impact of Timeline Algorithms on Opinion Dynamics Using Low-rank Updates Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We use our model to study the problem of minimizing the polarization and disagreement; we assume that we are allowed to make small changes to the users’ timeline compositions by strengthening some topics of discussion and penalizing some others. We present a gradient descent-based algorithm for this problem, and show that under realistic parameter settings, our algorithm computes a (1+varepsilon)-approximate solution in time~tO(msqrtn \l{}g(1/varepsilon)), where m~is the number of edges in the graph and n~is the number of vertices. |
Tianyi Zhou; Stefan Neumann; Kiran Garimella; Aristides Gionis; |
| 75 | On The Feasibility of Simple Transformer for Dynamic Graph Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we harness the strength of the Transformer’s self-attention mechanism, known for adeptly handling long-range dependencies in sequence modeling. |
Yuxia Wu; Yuan Fang; Lizi Liao; |
| 76 | Graph Contrastive Learning Reimagined: Exploring Universality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel GCL framework, named gRaph cOntraStive Exploring uNiversality (ROSEN), designed to achieve this objective. |
Jiaming Zhuo; Can Cui; Kun Fu; Bingxin Niu; Dongxiao He; Chuan Wang; Yuanfang Guo; Zhen Wang; Xiaochun Cao; Liang Yang; |
| 77 | Contrastive Fingerprinting: A Novel Website Fingerprinting Attack Over Few-shot Traces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a high-accuracy WF attack named Contrastive Fingerprinting (CF), which leverages contrastive learning and data augmentation over a few training traces. |
Yi Xie; Jiahao Feng; Wenju Huang; Yixi Zhang; Xueliang Sun; Xiaochou Chen; Xiapu Luo; |
| 78 | Graph Anomaly Detection with Bi-level Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study discovers that nodes with different classes yet similar neighbor label distributions (NLD) tend to have opposing loss curves, which we term it as loss rivalry. By introducing Contextual Stochastic Block Model (CSBM) and defining NLD distance, we explain this phenomenon theoretically and propose a Bi-level optimization Graph Neural Network (BioGNN), based on these observations. |
Yuan Gao; Junfeng Fang; Yongduo Sui; Yangyang Li; Xiang Wang; Huamin Feng; Yongdong Zhang; |
| 79 | PAGE: Equilibrate Personalization and Generalization in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the first algorithm to balance personalization and generalization on top of game theory, dubbed PAGE, which reshapes FL as a co-opetition game between clients and the server. |
Qian Chen; Zilong Wang; Jiaqi Hu; Haonan Yan; Jianying Zhou; Xiaodong Lin; |
| 80 | VilLain: Self-Supervised Learning on Homogeneous Hypergraphs Without Features Via Virtual Label Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, in this work, we propose VilLain, a novel self-supervised hypergraph representation learning method based on the propagation of virtual labels (v-labels). |
Geon Lee; Soo Yong Lee; Kijung Shin; |
| 81 | A Multifaceted Look at Starlink Performance Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we conduct a first-of-its-kind extensive multi-faceted analysis of Starlink performance leveraging several measurement sources. |
Nitinder Mohan; Andrew E. Ferguson; Hendrik Cech; Rohan Bose; Prakita Rayyan Renatin; Mahesh K. Marina; J\{o}rg Ott; |
| 82 | CapAlign: Improving Cross Modal Alignment Via Informative Captioning for Harmful Meme Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, researchers transformed images into textual captions and used language models for predictions, resulting in non-informative image captions. To address these gaps, this paper proposes an instructions-based abstracting approach CapAlign, in zero-shot visual question-answering settings. |
Junhui Ji; Xuanrui Lin; Usman Naseem; |
| 83 | A Worldwide View on The Reachability of Encrypted DNS Services Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the first comprehensive worldwide view of DoE service reachability. |
Ruixuan Li; Baojun Liu; Chaoyi Lu; Haixin Duan; Jun Shao; |
| 84 | List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: First, it is hard to share the contextual information of the ranking list between the two tasks. Second, the separate pipeline usually meets the error accumulation problem, where the small error from the reranking stage can largely affect the truncation stage. To solve these problems, we propose a Reranking-Truncation joint model (GenRT) that can perform the two tasks concurrently. |
Shicheng Xu; Liang Pang; Jun Xu; Huawei Shen; Xueqi Cheng; |
| 85 | Disambiguated Node Classification with Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These minority regions often exhibit irregular homophily/heterophily patterns and diverse neighborhood class distributions, resulting in ambiguity. In this work, we investigate the ambiguity problem within GNNs, its impact on representation learning, and the development of richer supervision signals to fight against this problem. |
Tianxiang Zhao; Xiang Zhang; Suhang Wang; |
| 86 | Weakly Supervised Anomaly Detection Via Knowledge-Data Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel framework, Knowledge-Data Alignment (KDAlign), to integrate rule knowledge, typically summarized by human experts, to supplement the limited labeled data. |
Haihong Zhao; Chenyi Zi; Yang Liu; Chen Zhang; Yan Zhou; Jia Li; |
| 87 | E2Usd: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose E2Usd that enables efficient-yet-accurate unsupervised MTS state detection. |
Zhichen Lai; Huan Li; Dalin Zhang; Yan Zhao; Weizhu Qian; Christian S. Jensen; |
| 88 | Question Difficulty Consistent Knowledge Tracing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to replace question IDs with question difficulty levels in deep knowledge tracing models. |
Guimei Liu; Huijing Zhan; Jung-jae Kim; |
| 89 | ZipZap: Efficient Training of Language Models for Large-Scale Fraud Detection on Blockchain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present ZipZap, a framework tailored to achieve both parameter and computational efficiency when training LMs on large-scale transaction data. |
Sihao Hu; Tiansheng Huang; Ka-Ho Chow; Wenqi Wei; Yanzhao Wu; Ling Liu; |
| 90 | Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Understanding this process – the strategies, incentives, and interactions involved in the development of AI tools – is crucial for making conclusions about societal implications and regulatory responses, and may provide insights beyond AI about general-purpose technologies. We propose a model of this adaptation process. |
Benjamin Laufer; Jon Kleinberg; Hoda Heidari; |
| 91 | Full-Attention Driven Graph Contrastive Learning: with Effective Mutual Information Insight Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their potential, employing full-attention graph Transformers for contrastive learning can introduce issues such as noisy redundancies. In this work, we propose the Graph Attention Contrastive Learning (GACL) model, which innovatively combines a full-attention transformer with a message-passing graph neural network as its encoder. |
Long Li; Zemin Liu; Chenghao Liu; Jianling Sun; |
| 92 | Breaking The Trilemma of Privacy, Utility, and Efficiency Via Controllable Machine Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenges, we present Controllable Machine Unlearning (ConMU), a novel framework designed to facilitate the calibration of MU. |
Zheyuan Liu; Guangyao Dou; Eli Chien; Chunhui Zhang; Yijun Tian; Ziwei Zhu; |
| 93 | HD-KT: Advancing Robust Knowledge Tracing Via Anomalous Learning Interaction Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we introduce two detectors for anomalous learning interactions, namely knowledge state-guided anomaly detector and student profile-guided anomaly detector. |
Haiping Ma; Yong Yang; Chuan Qin; Xiaoshan Yu; Shangshang Yang; Xingyi Zhang; Hengshu Zhu; |
| 94 | User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the Dual Cold-Start Cross Domain Recommendation (Dual-CSCDR) problem. |
Weiming Liu; Chaochao Chen; Xinting Liao; Mengling Hu; Jiajie Su; Yanchao Tan; Fan Wang; |
| 95 | A Counterfactual Framework for Learning and Evaluating Explanations for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the field of recommender systems, explainability remains a pivotal yet challenging aspect. To address this, we introduce the Learning to eXplain Recommendations (LXR) framework, a post-hoc, model-agnostic approach designed for providing counterfactual explanations. |
Oren Barkan; Veronika Bogina; Liya Gurevitch; Yuval Asher; Noam Koenigstein; |
| 96 | A Data-Centric Multi-Objective Learning Framework for Responsible Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, current methods encounter two challenges: difficulty in scaling to heterogeneous objectives within a unified framework, and inadequate controllability over objective priority during optimization, leading to uncontrollable solutions.In this paper, we present a data-centric optimization framework, MoRec, which unifies the learning of diverse objectives. |
Xu Huang; Jianxun Lian; Hao Wang; Hao Liao; Defu Lian; Xing Xie; |
| 97 | APT-Pipe: A Prompt-Tuning Tool for Social Data Annotation Using ChatGPT Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Yet these largely rely on manual effort and prior knowledge of the dataset being annotated. To address this limitation, we propose APT-Pipe, an automated prompt-tuning pipeline. |
Yiming Zhu; Zhizhuo Yin; Gareth Tyson; Ehsan-Ul Haq; Lik-Hang Lee; Pan Hui; |
| 98 | Prior-Free Mechanism with Welfare Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the problem of designing prior-free revenue-maximizing mechanisms for allocating items to n buyers when the mechanism is additionally provided with an estimate for the optimal welfare (which is guaranteed to be correct to within a multiplicative factor of 1/a). |
Guru Guruganesh; Jon Schneider; Joshua Wang; |
| 99 | Advancing Web 3.0: Making Smart Contracts Smarter on Blockchain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To handle the high complexity of model inference, we propose an on-chain and off-chain joint execution model, which separates the SMART contract into two parts: the deterministic code still runs inside an on-chain virtual machine, while the complex model inference is offloaded to off-chain compute nodes. |
Junqin Huang; Linghe Kong; Guanjie Cheng; Qiao Xiang; Guihai Chen; Gang Huang; Xue Liu; |
| 100 | T3RD: Test-Time Training for Rumor Detection on Social Media Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, when facing emergent events or rumors propagated in different languages, the performance of these models is significantly degraded, due to the lack of training data and prior knowledge (low resource). To tackle this challenge, we introduce the Test-Time Training for Rumor Detection (T^3RD) to enhance the performance of rumor detection models on low-resource datasets. |
Huaiwen Zhang; Xinxin Liu; Qing Yang; Yang Yang; Fan Qi; Shengsheng Qian; Changsheng Xu; |
| 101 | Inductive Graph Alignment Prompt: Bridging The Gap Between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to generalize graph pre-training to inductive scenario where the fine-tuning graphs might significantly differ from pre-training ones, we propose a novel graph prompt based method called Inductive Graph Alignment Prompt(IGAP). |
Yuchen Yan; Peiyan Zhang; Zheng Fang; Qingqing Long; |
| 102 | Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate whether users with varied levels of interest diversity are treated fairly. |
Yuying Zhao; Minghua Xu; Huiyuan Chen; Yuzhong Chen; Yiwei Cai; Rashidul Islam; Yu Wang; Tyler Derr; |
| 103 | Debiasing Recommendation with Personal Popularity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As such, we propose a user-aware version of item popularity named personal popularity (PP), which identifies different popular items for each user by considering the users that share similar interests. |
Wentao Ning; Reynold Cheng; Xiao Yan; Ben Kao; Nan Huo; Nur Al Hasan Haldar; Bo Tang; |
| 104 | GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although several methods applying LLMs to graphs have been proposed, they fail to simultaneously handle the pre-defined and open-ended tasks, with LLM as a node feature enhancer or as a standalone predictor. To break this dilemma, we propose to bridge the pretrained GM and LLM by a Translator, named GraphTranslator, aiming to leverage GM to handle the pre-defined tasks effectively and utilize the extended interface of LLMs to offer various open-ended tasks for GM. |
Mengmei Zhang; Mingwei Sun; Peng Wang; Shen Fan; Yanhu Mo; Xiaoxiao Xu; Hong Liu; Cheng Yang; Chuan Shi; |
| 105 | Clickbait Vs. Quality: How Engagement-Based Optimization Shapes The Content Landscape in Online Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To understand the total impact on the content landscape, we study a game between content creators competing on the basis of engagement metrics and analyze the equilibrium decisions about investment in quality and gaming. |
Nicole Immorlica; Meena Jagadeesan; Brendan Lucier; |
| 106 | UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, learning an effective cross-domain model presents the following challenges. First, various domains exhibit disparities in data characteristics, e.g., the number of variables, posing hurdles for existing models that impose inflexible constraints on these factors. Second, the model may encounter difficulties in distinguishing data from various domains, leading to suboptimal performance in our assessments. Third, the diverse convergence rates of time series domains can also result in compromised empirical performance. To address these issues, we propose UniTime for effective cross-domain time series learning. |
Xu Liu; Junfeng Hu; Yuan Li; Shizhe Diao; Yuxuan Liang; Bryan Hooi; Roger Zimmermann; |
| 107 | How Few Davids Improve One Goliath: Federated Learning in Resource-Skewed Edge Computing Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose RecipFL, a novel framework featuring a server-side graph hypernetwork. |
Jiayun Zhang; Shuheng Li; Haiyu Huang; Zihan Wang; Xiaohan Fu; Dezhi Hong; Rajesh K. Gupta; Jingbo Shang; |
| 108 | Cardinality Counting in Alcatraz: A Privacy-aware Federated Learning Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study introduces an innovative privacy-centric solution for the cardinality counting dilemma, leveraging a federated learning framework. |
Nan Wu; Xin Yuan; Shuo Wang; Hongsheng Hu; Minhui Xue; |
| 109 | Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we suggest the HIN is partitioned into private HINs stored on the client side and shared HINs on the server. |
Bo Yan; Yang Cao; Haoyu Wang; Wenchuan Yang; Junping Du; Chuan Shi; |
| 110 | Harnessing Large Language Models for Text-Rich Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Particularly, drawing inspiration from the successful application of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) models in user modeling, we introduce two unique summarization techniques in this paper, respectively hierarchical summarization and recurrent summarization. |
Zhi Zheng; WenShuo Chao; Zhaopeng Qiu; Hengshu Zhu; Hui Xiong; |
| 111 | Efficiency of The Generalized Second-Price Auction for Value Maximizers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the price of anarchy of the generalized second-price auction where bidders are value maximizers (i.e., autobidders). |
Yuan Deng; Mohammad Mahdian; Jieming Mao; Vahab Mirrokni; Hanrui Zhang; Song Zuo; |
| 112 | Non-uniform Bid-scaling and Equilibria for Different Auctions: An Empirical Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To complement theoretical understandings, we empirically study how different auction formats (FPA, GSP, VCG) with different levels of non-uniform bid-scaling perform in an autobidding world with a synthetic dataset for auctions. |
Yuan Deng; Jieming Mao; Vahab Mirrokni; Yifeng Teng; Song Zuo; |
| 113 | Off-Policy Evaluation of Slate Bandit Policies Via Optimizing Abstraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We study off-policy evaluation (OPE) in the problem of slate contextual bandits where a policy selects multi-dimensional actions known as slates. |
Haruka Kiyohara; Masahiro Nomura; Yuta Saito; |
| 114 | Metacognitive Retrieval-Augmented Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces MetaRAG, an approach that combines the retrieval-augmented generation process with metacognition. |
Yujia Zhou; Zheng Liu; Jiajie Jin; Jian-Yun Nie; Zhicheng Dou; |
| 115 | Cognitive Personalized Search Integrating Large Language Models with An Efficient Memory Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a Cognitive Personalized Search (CoPS) model, which integrates Large Language Models (LLMs) with a cognitive memory mechanism inspired by human cognition. |
Yujia Zhou; Qiannan Zhu; Jiajie Jin; Zhicheng Dou; |
| 116 | BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, such a decoupled approach often inhibits optimal information exchange between these intertwined tasks. Therefore, we present BOND, which bootstraps the local and global informative signals to promote each other in an end-to-end regime. |
Yuqing Cheng; Bo Chen; Fanjin Zhang; Jie Tang; |
| 117 | SMUG: Sand Mixing for Unobserved Class Detection in Graph Few-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents SMUG, a novel GFSL framework that can detect unobserved classes. |
Chenxu Wang; Xichan Nie; Jinfeng Chen; Pinghui Wang; Junzhou Zhao; Xiaohong Guan; |
| 118 | Endowing Pre-trained Graph Models with Provable Fairness Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, most of them lack a theoretical guarantee, i.e., provable lower bounds on the fairness of model predictions, which directly provides assurance in a practical scenario. To overcome these limitations, we propose a novel adapter-tuning framework that endows pre-trained Graph models with Provable fAiRness (called GraphPAR). |
Zhongjian Zhang; Mengmei Zhang; Yue Yu; Cheng Yang; Jiawei Liu; Chuan Shi; |
| 119 | More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these approaches ignore the motion details contained in the GPS data, limiting the representation capability of trajectory representation learning. To fill this gap, we propose a novel representation learning framework that is Jointly G PS and Route Modeling based on self-supervised technology, namely JGRM. |
Zhipeng Ma; Zheyan Tu; Xinhai Chen; Yan Zhang; Deguo Xia; Guyue Zhou; Yilun Chen; Yu Zheng; Jiangtao Gong; |
| 120 | Generating Multi-turn Clarification for Web Information Seeking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we make a step to extend the multi-turn clarification generation to Web search for clarifying users’ ambiguous or faceted intents. |
Ziliang Zhao; Zhicheng Dou; |
| 121 | GAUSS: GrAph-customized Universal Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, learning the graph via universal GNNs is disabled in SSL, since their distinguishability on homophilic and heterophilic edges disappears without the labels. To overcome this difficulty, this paper proposes novel GrAph-customized Universal Self-Supervised Learning (GAUSS) by exploiting local attribute distribution. |
Liang Yang; Weixiao Hu; Jizhong Xu; Runjie Shi; Dongxiao He; Chuan Wang; Xiaochun Cao; Zhen Wang; Bingxin Niu; Yuanfang Guo; |
| 122 | Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We hypothesize that in scenarios where multimodal information is pertinent, the clarification process can be improved by using non-textual information. Therefore, we propose to add images to clarifying questions and formulate the novel task of asking multimodal clarifying questions in open-domain, mixed-initiative conversational search systems. |
Yifei Yuan; Clemencia Siro; Mohammad Aliannejadi; Maarten de Rijke; Wai Lam; |
| 123 | BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes a blockchainbased fully decentralized P2P framework for FL, called BlockDFL. |
Zhen Qin; Xueqiang Yan; Mengchu Zhou; Shuiguang Deng; |
| 124 | PaCEr: Network Embedding From Positional to Structural Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we seek to demystify the underlying relationship between positional embedding and structural embedding. |
Yuchen Yan; Yongyi Hu; Qinghai Zhou; Lihui Liu; Zhichen Zeng; Yuzhong Chen; Menghai Pan; Huiyuan Chen; Mahashweta Das; Hanghang Tong; |
| 125 | General Debiasing for Graph-based Collaborative Filtering Via Adversarial Graph Dropout Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the current aggregation approach combines all information, both biased and unbiased, leading to biased representation learning. Consequently, graph-based recommenders can learn distorted views of users/items, hindering the modeling of their true preferences and generalizations.To address this issue, we introduce a novel framework called Adversarial Graph Dropout (AdvDrop). |
An Zhang; Wenchang Ma; Pengbo Wei; Leheng Sheng; Xiang Wang; |
| 126 | Poisoning Federated Recommender Systems with Fake Users Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel fake user based poisoning attack named PoisonFRS to promote the attacker-chosen targeted item in federated recommender systems without requiring knowledge about user-item rating data, user attributes, or the aggregation rule used by the server. |
Ming Yin; Yichang Xu; Minghong Fang; Neil Zhenqiang Gong; |
| 127 | Enhancing Complex Question Answering Over Knowledge Graphs Through Evidence Pattern Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural dependencies during subgraph extraction. |
Wentao Ding; Jinmao Li; Liangchuan Luo; Yuzhong Qu; |
| 128 | Memory Disagreement: A Pseudo-Labeling Measure from Training Dynamics for Semi-supervised Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose memory disagreement (MoDis for short), a novel uncertainty measure for pseudo-labeling. |
Hongbin Pei; Yuheng Xiong; Pinghui Wang; Jing Tao; Jialun Liu; Huiqi Deng; Jie Ma; Xiaohong Guan; |
| 129 | Perennial Semantic Data Terms of Use for Decentralized Web Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel formal description of Data Terms of Use (DToU), along with a DToU reasoner. |
Rui Zhao; Jun Zhao; |
| 130 | A Simple But Effective Approach for Unsupervised Few-Shot Graph Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, they commonly adopt complicated meta-learning algorithms via episodic training to transfer prior knowledge from base classes. To break free from these constraints, in this paper, we propose a simple yet effective approach named SMART for unsupervised few-shot graph classification without using any labeled data. |
Yonghao Liu; Lan Huang; Bowen Cao; Ximing Li; Fausto Giunchiglia; Xiaoyue Feng; Renchu Guan; |
| 131 | AI for Materials Innovation: Self-Improving Photosensitizer Discovery System Via Bayesian Search with First-Principles Simulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Herein, we demonstrate how to combine accurate prediction of material performance via first-principles calculation and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PSs). |
Bin Liu; |
| 132 | Collaboration-Aware Hybrid Learning for Knowledge Development Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a Collaboration-Aware Hybrid Learning approach (CAHL) for predicting the future knowledge acquisition of employees and quantifying the impact of various knowledge learning patterns. |
Liyi Chen; Chuan Qin; Ying Sun; Xin Song; Tong Xu; Hengshu Zhu; Hui Xiong; |
| 133 | GAMMA: Graph Neural Network-Based Multi-Bottleneck Localization for Microservices Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces GAMMA, a novel, explainable graph learning model that integrates a mixture of experts to detect multiple bottlenecks. |
Gagan Somashekar; Anurag Dutt; Mainak Adak; Tania Lorido Botran; Anshul Gandhi; |
| 134 | Hierarchical Graph Signal Processing for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a hierarchical graph signal processing method (HiGSP) for collaborative filtering, which consists of two key modules: 1) the cluster-wise filter module that recognizes user unique interaction patterns merely from interactions of users with similar preferences, making the recognized patterns able to reflect user preference without being influenced by other users with different interaction behaviors, and 2) the globally-aware filter module that serves as a complementary to the cluster-wise filter module to recognize user general interaction patterns more effectively from all user interactions. |
Jiafeng Xia; Dongsheng Li; Hansu Gu; Tun Lu; Peng Zhang; Li Shang; Ning Gu; |
| 135 | Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose LLMQA, a generalized framework that formulates the ODQA process into three basic steps: query expansion, document selection, and answer generation, combining the superiority of both retrieval-based and generation-based evidence. |
Hongda Sun; Yuxuan Liu; Chengwei Wu; Haiyu Yan; Cheng Tai; Xin Gao; Shuo Shang; Rui Yan; |
| 136 | MARIO: Model Agnostic Recipe for Improving OOD Generalization of Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we investigate the problem of out-of-distribution (OOD) generalization for unsupervised learning methods on graph data. |
Yun Zhu; Haizhou Shi; Zhenshuo Zhang; Siliang Tang; |
| 137 | GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Concretely speaking, for example, the additional task-specific node information in downstream tasks (specificity) is usually deliberately omitted so that the pre-trained representation (transferability) can be leveraged. The trade-off as such is termed as transferability-specificity dilemma in this work. To address this challenge, we introduce an innovative deployment module coined as GraphControl, motivated by ControlNet, to realize better graph domain transfer learning. |
Yun Zhu; Yaoke Wang; Haizhou Shi; Zhenshuo Zhang; Dian Jiao; Siliang Tang; |
| 138 | Linear-Time Graph Neural Networks for Scalable Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches while maintaining GNNs’ powerful expressiveness for superior prediction accuracy. |
Jiahao Zhang; Rui Xue; Wenqi Fan; Xin Xu; Qing Li; Jian Pei; Xiaorui Liu; |
| 139 | UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from The Web Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To answer the questions, we leverage the power of Large Language Models (LLMs) and introduce the first-ever LLM-enhanced framework that integrates the knowledge of text modality into urban imagery, named LLM-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining (UrbanCLIP ). |
Yibo Yan; Haomin Wen; Siru Zhong; Wei Chen; Haodong Chen; Qingsong Wen; Roger Zimmermann; Yuxuan Liang; |
| 140 | Graph-Skeleton: ~1\% Nodes Are Sufficient to Represent Billion-Scale Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that properly fetching and condensing the background nodes from massive web graph data might be a more economical shortcut to tackle the obstacles fundamentally. |
Linfeng Cao; Haoran Deng; Yang Yang; Chunping Wang; Lei Chen; |
| 141 | Taxonomy Completion Via Implicit Concept Insertion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While there has been extensive work on expanding taxonomies from externally mined data, there has been less attention paid to enriching taxonomies by exploiting existing concepts and structure within the taxonomy. In this work, we show the usefulness of this kind of enrichment, and explore its viability with a new taxonomy completion system ICON (I mplicit CON cept Insertion). |
Jingchuan Shi; Hang Dong; Jiaoyan Chen; Zhe Wu; Ian Horrocks; |
| 142 | Unified Uncertainty Estimation for Cognitive Diagnosis Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. |
Fei Wang; Qi Liu; Enhong Chen; Chuanren Liu; Zhenya Huang; Jinze Wu; Shijin Wang; |
| 143 | InArt: In-Network Aggregation with Route Selection for Accelerating Distributed Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To accommodate traffic dynamics, InArt adopts a two-phase approach: splitting the training model among multiple parameter servers and selecting routing paths for INA. We propose Lagrange multiplier and randomized rounding algorithms for these phases, respectively. |
Jiawei Liu; Yutong Zhai; Gongming Zhao; Hongli Xu; Jin Fang; Zhen Zeng; Ying Zhu; |
| 144 | Unveiling The Paradox of NFT Prosperity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given that NFTs have generated significant media attention since 2021, we perform a large-scale measurement study of the NFT ecosystem. |
Jintao Huang; Pengcheng Xia; Jiefeng Li; Kai Ma; Gareth Tyson; Xiapu Luo; Lei Wu; Yajin Zhou; Wei Cai; Haoyu Wang; |
| 145 | Tight Competitive and Variance Analyses of Matching Policies in Gig Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we initiate variance analysis for online matching algorithms under KHD. |
Pan Xu; |
| 146 | GraphPro: Graph Pre-training and Prompt Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In our study, we propose a framework called GraphPro that combines dynamic graph pre-training with prompt learning in an efficient way. |
Yuhao Yang; Lianghao Xia; Da Luo; Kangyi Lin; Chao Huang; |
| 147 | Local Centrality Minimization with Quality Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We prove the NP-hardness of our model and that the most intuitive greedy algorithm has a quite limited performance in terms of approximation ratio. |
Atsushi Miyauchi; Lorenzo Severini; Francesco Bonchi; |
| 148 | Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods overlook the importance of semantic evolvement information of event in propagation process, which is often challenging to be truly learned in supervised training paradigms and traditional rumor detection methods. To address this issue, we propose a novel semantic evolvement enhanced Graph Autoencoder for Rumor Detection (GARD) model in this paper. |
Xiang Tao; Liang Wang; Qiang Liu; Shu Wu; Liang Wang; |
| 149 | MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Two main challenges arise when extending KD for node classification to graph classification: (1) The inherent sparsity of learning signals due to soft labels being generated at the graph level; (2) The limited expressiveness of student MLPs, especially in datasets with limited input feature spaces. To overcome these challenges, we introduce MuGSI, a novel KD framework that employs Multi-granularity Structural Information for graph classification. |
Tianjun Yao; Jiaqi Sun; Defu Cao; Kun Zhang; Guangyi Chen; |
| 150 | Fast Graph Condensation with Structure-based Neural Tangent Kernel Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose reforming the graph condensation problem as a Kernel Ridge Regression (KRR) task instead of iteratively training GNNs in the inner loop of bi-level optimization. |
Lin Wang; Wenqi Fan; Jiatong Li; Yao Ma; Qing Li; |
| 151 | Mining Exploratory Queries for Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper focuses on mining exploratory queries as additional options to meet users’ exploratory needs in conversational search systems. |
Wenhan Liu; Ziliang Zhao; Yutao Zhu; Zhicheng Dou; |
| 152 | Retention Depolarization in Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ReFair, the first computational framework that continuously improves recommendation algorithms while ensuring long-term retention fairness in the entire user population. |
Xiaoying Zhang; Hongning Wang; Yang Liu; |
| 153 | Graph Contrastive Learning with Kernel Dependence Maximization for Social Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the subtle distinctions between the augmented representations render CL methods sensitive to noise perturbations. Inspired by the Hilbert-Schmidt independence criterion (HSIC), we propose a graph Contrastive Learning model with Kernel Dependence Maximization CL-KDM for social recommendation to address these challenges. |
Xuelian Ni; Fei Xiong; Yu Zheng; Liang Wang; |
| 154 | Collaborate to Adapt: Source-Free Graph Domain Adaptation Via Bi-directional Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore the scenario of source-free unsupervised graph domain adaptation, which tries to address the domain adaptation problem without accessing the labelled source graph. |
Zhen Zhang; Meihan Liu; Anhui Wang; Hongyang Chen; Zhao Li; Jiajun Bu; Bingsheng He; |
| 155 | Dynamic Graph Information Bottleneck Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents the novelDynamic Graph Information Bottleneck (DGIB) framework to learn robust and discriminative representations. |
Haonan Yuan; Qingyun Sun; Xingcheng Fu; Cheng Ji; Jianxin Li; |
| 156 | Graph Contrastive Learning with Cohesive Subgraph Awareness Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a novel unified framework called CTAug, to seamlessly integrate cohesion awareness into various existing GCL mechanisms. |
Yucheng Wu; Leye Wang; Xiao Han; Han-Jia Ye; |
| 157 | ModelGo: A Practical Tool for Machine Learning License Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce ModelGo, a practical tool for auditing potential legal risks in machine learning projects to enhance compliance and fairness. |
Moming Duan; Qinbin Li; Bingsheng He; |
| 158 | Mirror Gradient: Towards Robust Multimodal Recommender Systems Via Exploring Flat Local Minima Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we analyze multimodal recommender systems from the novel perspective of flat local minima and propose a concise yet effective gradient strategy called Mirror Gradient (MG). |
Shanshan Zhong; Zhongzhan Huang; Daifeng Li; Wushao Wen; Jinghui Qin; Liang Lin; |
| 159 | Unveiling The Invisible: Detection and Evaluation of Prototype Pollution Gadgets with Dynamic Taint Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes Dasty, the first semi-automated pipeline to help developers identify gadgets in their applications’ software supply chain. |
Mikhail Shcherbakov; Paul Moosbrugger; Musard Balliu; |
| 160 | Navigating Multidimensional Ideologies with Reddit’s Political Compass: Economic Conflict and Social Affinity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we analyze social interactions on Reddit, under the lens of a multi-dimensional ideological framework: the political compass. |
Ernesto Colacrai; Federico Cinus; Gianmarco De Francisci Morales; Michele Starnini; |
| 161 | Can Small Language Models Be Good Reasoners for Sequential Recommendation? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Step-by-step knowLedge dIstillation fraMework for recommendation (SLIM), paving a promising path for sequential recommenders to enjoy the exceptional reasoning capabilities of LLMs in a slim (i.e. resource-efficient) manner. |
Yuling Wang; Changxin Tian; Binbin Hu; Yanhua Yu; Ziqi Liu; Zhiqiang Zhang; Jun Zhou; Liang Pang; Xiao Wang; |
| 162 | Span-Pair Interaction and Tagging for Dialogue-Level Aspect-Based Sentiment Quadruple Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These characteristics challenge existing methods that struggle to model explicit span-level interactions or have high computational costs. In this paper, we propose a span-pair interaction and tagging method to solve these issues, which includes a novel Span-pair Tagging Scheme (STS) and a simple and efficient Multi-level Representation Model (MRM). |
Changzhi Zhou; Zhijing Wu; Dandan Song; Linmei Hu; Yuhang Tian; Jing Xu; |
| 163 | Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Motivated by the recent advancements in sharpness-aware minimization (SAM), which considers the geometric aspects of the loss landscape during optimization, we present a dedicated optimizer crafted for CTR prediction, named Helen. |
Zirui Zhu; Yong Liu; Zangwei Zheng; Huifeng Guo; Yang You; |
| 164 | Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing discretized models require retraining for each prediction horizon, restricting their applicability. To tackle the above issues, we propose a neural Spatial-Temporal Delay Differential Equation model, namely STDDE. |
Qingqing Long; Zheng Fang; Chen Fang; Chong Chen; Pengfei Wang; Yuanchun Zhou; |
| 165 | Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing RL methods require to retrain RL agents even with a tiny change of the stock pool, which leads to high computational cost and unstable performance. To tackle this challenge, we propose EarnMore, a rEinforcement leARNing framework with Maskable stOck REpresentation to handle PM with CSPs through one-shot training in a global stock pool (GSP). |
Wentao Zhang; Yilei Zhao; Shuo Sun; Jie Ying; Yonggang Xie; Zitao Song; Xinrun Wang; Bo An; |
| 166 | Detecting and Understanding Self-Deleting JavaScript Code Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we systematically study the emerging client-side JavaScript self-deletion behavior on the web. |
Xinzhe Wang; Zeyang Zhuang; Wei Meng; James Cheng; |
| 167 | Causally Debiased Time-aware Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a causally debiased time-aware recommender framework to accurately learn user preference. |
Lei Wang; Chen Ma; Xian Wu; Zhaopeng Qiu; Yefeng Zheng; Xu Chen; |
| 168 | Unleashing The Power of Knowledge Graph for Recommendation Via Invariant Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the principle of invariance to the knowledge-aware recommendation, culminating in our Knowledge Graph Invariant Learning (KGIL) framework. |
Shuyao Wang; Yongduo Sui; Chao Wang; Hui Xiong; |
| 169 | When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this separation of the recommendation model and users’ private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings. This paper introduces a novel method called Item-aligned Federated Aggregation (IFedRec) to address this challenge. |
Chunxu Zhang; Guodong Long; Tianyi Zhou; Zijian Zhang; Peng Yan; Bo Yang; |
| 170 | Towards Cross-Table Masked Pretraining for Web Data Mining Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a pioneering endeavor, this work mainly (i)-contributes a high-quality real-world tabular dataset, (ii)-proposes an innovative, generic, and efficient cross-table pretraining framework, dubbed as CM2, where the core to it comprises a semantic-aware tabular neural network that uniformly encodes heterogeneous tables without much restriction and (iii)-introduces a novel pretraining objective — prompt Masked Table Modeling (pMTM) — inspired by NLP but intricately tailored to scalable pretraining on tables. |
Chao Ye; Guoshan Lu; Haobo Wang; Liyao Li; Sai Wu; Gang Chen; Junbo Zhao; |
| 171 | The Dynamics of (Not) Unfollowing Misinformation Spreaders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Overall, we observe a strong persistence of misinformation ties. The fact that users rarely unfollow misinformation spreaders suggests a need for external nudges and the importance of preventing exposure from arising in the first place. |
Joshua Ashkinaze; Eric Gilbert; Ceren Budak; |
| 172 | MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Mulan, a unified multi-modal causal structure learning method designed to identify root causes in microservice systems. |
Lecheng Zheng; Zhengzhang Chen; Jingrui He; Haifeng Chen; |
| 173 | Bridging The Space Gap: Unifying Geometry Knowledge Graph Embedding with Optimal Transport Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel and highly effective approach called Unified Geometry Knowledge Graph Embedding (UniGE) to address the challenge of representing diverse geometric data in KGs. |
Yuhan Liu; Zelin Cao; Xing Gao; Ji Zhang; Rui Yan; |
| 174 | Not All Embeddings Are Created Equal: Towards Robust Cross-domain Recommendation Via Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the recent development of contrastive learning, this paper proposes User-aware Contrastive Learning for Robust cross-domain recommendation (UCLR), enhancing the robustness of cross-domain recommendation. |
Wenhao Yang; Yingchun Jian; Yibo Wang; Shiyin Lu; Lei Shen; Bing Wang; Haihong Tang; Lijun Zhang; |
| 175 | Towards Deeper Understanding of PPR-based Embedding Approaches: A Topological Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Then, we study whether the embeddings generated by this strategy can be inverted to better recover the graph topology information than random-walk based embeddings. To achieve this, we propose two methods for recovering graph topology via PPR-based embeddings, including the analytical method and the optimization method. |
Xingyi Zhang; Zixuan Weng; Sibo Wang; |
| 176 | PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, two key challenges in multi-modal recommenders remain unresolved: i) The introduction of multi-modal encoders with a large number of additional parameters causes overfitting, given high-dimensional multi-modal features provided by extractors (e.g., ViT, BERT). ii) Side information inevitably introduces inaccuracies and redundancies, which skew the modality-interaction dependency from reflecting true user preference. To tackle these problems, we propose to simplify and empower recommenders through Multi-modal Knowledge Distillation (PromptMM) with the prompt-tuning that enables adaptive quality distillation. |
Wei Wei; Jiabin Tang; Lianghao Xia; Yangqin Jiang; Chao Huang; |
| 177 | A Fast Hop-Biased Approximation Algorithm for The Quadratic Group Steiner Tree Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approximation algorithm for QGSTP called HB. |
Xiaoqing Wang; Gong Cheng; |
| 178 | Self-Guided Robust Graph Structure Refinement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a self-guided GSR framework (SG-GSR), which utilizes a clean sub-graph found within the given attacked graph itself. |
Yeonjun In; Kanghoon Yoon; Kibum Kim; Kijung Shin; Chanyoung Park; |
| 179 | Towards Efficient Communication and Secure Federated Recommendation System Via Low-rank Training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In response, we propose a novel framework, called Correlated Low-rank Structure (CoLR), which leverages the concept of adjusting lightweight trainable parameters while keeping most parameters frozen. |
Ngoc-Hieu Nguyen; Tuan-Anh Nguyen; Tuan Nguyen; Vu Tien Hoang; Dung D. Le; Kok-Seng Wong; |
| 180 | Full-stage Diversified Recommendation: Large-scale Online Experiments in Short-video Platform Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Last, the impact of diversity perception on user engagement stresses the necessity of explicit diversity modeling. To address these challenges in industrial systems, in this work, we deploy several existing diversified algorithms in a real-world short-video platform, including exploration-exploitation, feature-aware debiasing, and diversity optimization. |
Nian Li; Yunzhu Pan; Chen Gao; Depeng Jin; Qingmin Liao; |
| 181 | Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Symbolic Graph Ranker (SGR), which aims to take advantage of both text-based and graph-based approaches by leveraging the power of recent Large Language Models (LLMs). |
Songhao Wu; Quan Tu; Hong Liu; Jia Xu; Zhongyi Liu; Guannan Zhang; Ran Wang; Xiuying Chen; Rui Yan; |
| 182 | GraphLeak: Patient Record Leakage Through Gradients with Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we present GraphLeak, which incorporates the medical knowledge graph in gradient leakage attacks. |
Xi Sheryl Zhang; Weifan Guan; Jiahao Lu; Zhaopeng Qiu; Jian Cheng; Xian Wu; Yefeng Zheng; |
| 183 | Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating The Over-smoothing Problem Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a cross-space adaptive filter, called CSF, to produce the adaptive-frequency information extracted from both the topology and attribute spaces. |
Chen Huang; Haoyang Li; Yifan Zhang; Wenqiang Lei; Jiancheng Lv; |
| 184 | Perceptions in Pixels: Analyzing Perceived Gender and Skin Tone in Real-world Image Search Results Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The results returned by image search engines have the power to shape peoples’ perceptions about social groups. Existing work on image search engines leverages hand-selected queries for occupations like doctor and engineer to quantify racial and gender bias in search results. We complement this work by analyzing peoples’ real-world image search queries and measuring the distributions of perceived gender, skin tone, and age in their results. |
Jeffrey Gleason; Avijit Ghosh; Ronald E. Robertson; Christo Wilson; |
| 185 | Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we view the diffusion process as a continuous-time dynamical system, based on which we establish a continuous-time diffusion model. |
Keke Huang; Ruize Gao; Bogdan Cautis; Xiaokui Xiao; |
| 186 | Explainable Fake News Detection with Large Language Model Via Defense Among Competing Wisdom Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To detect fake news from a sea of diverse, crowded and even competing narratives, in this paper, we propose a novel defense-based explainable fake news detection framework. |
Bo Wang; Jing Ma; Hongzhan Lin; Zhiwei Yang; Ruichao Yang; Yuan Tian; Yi Chang; |
| 187 | Exploring Neural Scaling Law and Data Pruning Methods For Node Classification on Large-scale Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, we first explore the neural scaling law for node classification tasks on three large-scale graphs. Then, we benchmark several state-of-the-art data pruning methods on these tasks, not only validating the possibility of improving the original unsatisfactory power law but also gaining insights into a hard-and-representative principle on picking an effective subset of training nodes. Moreover, we leverage the transductive setting to propose a novel data pruning method, which instantiates our principle in a test set-targeted manner. |
Zhen Wang; Yaliang Li; Bolin Ding; Yule Li; Zhewei Wei; |
| 188 | Co-clustering for Federated Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we delve into the inefficiencies of the K-Means method in client grouping, attributing failures due to the high dimensionality as well as data sparsity occurring in FRS, and propose CoFedRec, a novel Co-clustering Federated Recommendation mechanism, to address clients heterogeneity and enhance the collaborative filtering within the federated framework. |
Xinrui He; Shuo Liu; Jacky Keung; Jingrui He; |
| 189 | RicciNet: Deep Clustering Via A Riemannian Generative Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Deep clustering in Riemannian manifold still faces significant challenges: (1) Ricci flow itself is unaware of cluster membership, (2) Ricci curvature prevents the gradient backpropagation, and (3) learning the flow largely remains open in the manifold. To bridge these gaps, we propose a novel Riemannian generative model (RicciNet), a neural Ricci flow with several theoretical guarantees. |
Li Sun; Jingbin Hu; Suyang Zhou; Zhenhao Huang; Junda Ye; Hao Peng; Zhengtao Yu; Philip Yu; |
| 190 | LinkNER: Linking Local Named Entity Recognition Models to Large Language Models Using Uncertainty Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, non-public and large-scale weights make tuning LLMs difficult. To address these challenges, we propose a framework that combines small fine-tuned models with LLMs (LinkNER) and an uncertainty-based linking strategy called RDC that enables fine-tuned models to complement black-box LLMs, achieving better performance. |
Zhen Zhang; Yuhua Zhao; Hang Gao; Mengting Hu; |
| 191 | Robust Decision Aggregation with Second-order Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider a decision aggregation problem with two experts who each make a binary recommendation after observing a private signal about an unknown binary world state. |
Yuqi Pan; Zhaohua Chen; Yuqing Kong; |
| 192 | Generative News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel generative news recommendation paradigm that includes two steps: (1) Leveraging the internal knowledge and reasoning capabilities of the Large Language Model (LLM) to perform high-level matching between candidate news and user representation; (2) Generating a coherent and logically structured narrative based on the associations between related news and user interests, thus engaging users in further reading of the news. |
Shen Gao; Jiabao Fang; Quan Tu; Zhitao Yao; Zhumin Chen; Pengjie Ren; Zhaochun Ren; |
| 193 | Game-theoretic Counterfactual Explanation for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a semivalue-based, non-learning approach to generate CFE for node classification tasks, eliminating the need for any additional training. |
Chirag Chhablani; Sarthak Jain; Akshay Channesh; Ian A. Kash; Sourav Medya; |
| 194 | Calibrating Graph Neural Networks from A Data-centric Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that the miscalibration of GNNs may stem from the graph data and can be alleviated through topology modification. |
Cheng Yang; Chengdong Yang; Chuan Shi; Yawen Li; Zhiqiang Zhang; Jun Zhou; |
| 195 | LFDe: A Lighter, Faster and More Data-Efficient Pre-training Framework for Event Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing pre-training methods necessitate substantial computational resources, requiring high-performance hardware infrastructure and extensive training duration. In response to these challenges, this paper proposes a Lighter, Faster, and more Data-efficient pre-training framework for EE, named LFDe. |
Zhigang Kan; Liwen Peng; Yifu Gao; Ning Liu; Linbo Qiao; Dongsheng Li; |
| 196 | Graph Fairness Learning Under Distribution Shifts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Will graph fairness performance decrease under distribution shifts? How does distribution shifts affect graph fairness learning? All these open questions are largely unexplored from a theoretical perspective. To answer these questions, we first theoretically identify the factors that determine bias on a graph. Subsequently, we explore the factors influencing fairness on testing graphs, with a noteworthy factor being the representation distances of certain groups between the training and testing graph. |
Yibo Li; Xiao Wang; Yujie Xing; Shaohua Fan; Ruijia Wang; Yaoqi Liu; Chuan Shi; |
| 197 | PMG�: Personalized Multimodal Generation with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Multi-modal generation attracts great attention from both the industry and academia, but there is little work on personalized generation, which has important applications such as recommender systems. This paper proposes the first method for personalized multimodal generation using LLMs, showcases its applications and validates its performance via an extensive experimental study on two datasets. |
Xiaoteng Shen; Rui Zhang; Xiaoyan Zhao; Jieming Zhu; Xi Xiao; |
| 198 | QUIC Is Not Quick Enough Over Fast Internet Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct a systematic examination of QUIC’s performance over high-speed networks. |
Xumiao Zhang; Shuowei Jin; Yi He; Ahmad Hassan; Z. Morley Mao; Feng Qian; Zhi-Li Zhang; |
| 199 | Multimodal Relation Extraction Via A Mixture of Hierarchical Visual Context Learners Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, research to date has largely ignored the understanding of how hierarchical visual semantics are represented and the characteristics that can benefit relation extraction. To bridge this gap, we propose a novel two-stage hierarchical visual context fusion transformer incorporating the mixture of multimodal experts framework to effectively represent and integrate hierarchical visual features into textual semantic representations. |
Xiyang Liu; Chunming Hu; Richong Zhang; Kai Sun; Samuel Mensah; Yongyi Mao; |
| 200 | Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Intelligent Education Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students’ mastery levels inference in WOIESs. |
Shuo Liu; Junhao Shen; Hong Qian; Aimin Zhou; |
| 201 | HSDirSniper: A New Attack Exploiting Vulnerabilities in Tor’s Hidden Service Directories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our investigation reveals that the latest iteration of Tor hidden services still exhibits vulnerabilities related to Hidden Service Directories (HSDirs). Building upon this identified weakness, we introduce the HSDirSniper attack, which leverages a substantial volume of descriptors to inundate the HSDir’s descriptor cache. |
Qingfeng Zhang; Zhiyang Teng; Xuebin Wang; Yue Gao; Qingyun Liu; Jinqiao Shi; |
| 202 | Trident: A Universal Framework for Fine-Grained and Class-Incremental Unknown Traffic Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The challenges are mainly concerned with (i) fine-grained emerging attack detection and (ii) incremental updates/adaptations. To tackle these problems, we propose to decouple the need for model capabilities by transforming known/new class identification issues into multiple independent one-class learning tasks. |
Ziming Zhao; Zhaoxuan Li; Zhuoxue Song; Wenhao Li; Fan Zhang; |
| 203 | Query2GMM: Learning Representation with Gaussian Mixture Model for Reasoning Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To better model queries with diversified answers, we propose Query2GMM for answering logical queries over knowledge graphs. |
Yuhan Wu; Yuanyuan Xu; Wenjie Zhang; Xiwei Xu; Ying Zhang; |
| 204 | SymLearn: A Symbiotic Crowd-AI Collective Learning Framework to Web-based Healthcare Policy Adherence Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper develops a symbiotic human-AI collective learning framework that explores the complementary strengths of both AI and crowdsourced human intelligence to address a novel Web-based healthcare-policy-adherence assessment (WebHA) problem. |
Yang Zhang; Ruohan Zong; Lanyu Shang; Huimin Zeng; Zhenrui Yue; Dong Wang; |
| 205 | Supervised Fine-Tuning for Unsupervised KPI Anomaly Detection for Mobile Web Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose AnoTuner, which incorporates a false negative augmentation mechanism to generate similar false negative feedback cases, effectively compensating for the low feedback frequency. |
Zhaoyang Yu; Shenglin Zhang; Mingze Sun; Yingke Li; Yankai Zhao; Xiaolei Hua; Lin Zhu; Xidao Wen; Dan Pei; |
| 206 | COLA: Cross-city Mobility Transformer for Human Trajectory Simulation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we are motivated to explore the intriguing problem of mobility transfer across cities, grasping the universal patterns of human trajectories to augment the powerful Transformer with external mobility data. |
Yu Wang; Tongya Zheng; Yuxuan Liang; Shunyu Liu; Mingli Song; |
| 207 | Multi-Scenario Pricing for Hotel Revenue Management Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: So we propose a Multi Scenario Pricing model (MSP) with a novel sharing structure design that leverages cross-scenario and specific information to capture more accurate market demand and competitiveness. |
Wendong Xiao; Shuqi Zhang; Zhiyi Huang; Yao Yu; |
| 208 | RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, in order to let the PLM effectively understand each category, we at first propose a novel form of rule-based knowledge using logical expressions to characterize the meanings of categories. Then, we develop a prompting PLM-based approach named RulePrompt for the WSTC task, consisting of a rule mining module and a rule-enhanced pseudo label generation module, plus a self-supervised fine-tuning module to make the PLM align with this task. |
Miaomiao Li; Jiaqi Zhu; Yang Wang; Yi Yang; Yilin Li; Hongan Wang; |
| 209 | Cooperative Classification and Rationalization for Graph Generalization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, extracting rationales is difficult due to limited learning signals, resulting in less accurate rationales and diminished predictions. To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of theclassification and therationalization module. |
Linan Yue; Qi Liu; Ye Liu; Weibo Gao; Fangzhou Yao; Wenfeng Li; |
| 210 | Multimodal Query Suggestion with Multi-Agent Reinforcement Learning from Human Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present the RL4Sugg framework, leveraging the power of Large Language Models (LLMs) with Multi-Agent Reinforcement Learning from Human Feedback to optimize the generation process. |
Zheng Wang; Bingzheng Gan; Wei Shi; |
| 211 | Graph Principal Flow Network for Conditional Graph Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel graph conditional generative model, Graph Principal Flow Network (GPrinFlowNet), which enables us to progressively generate high-quality graphs from low- to high-frequency components for a given graph label. |
Zhanfeng Mo; Tianze Luo; Sinno Jialin Pan; |
| 212 | Identifying VPN Servers Through Graph-Represented Behaviors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose VPNChecker, which utilizes the graph-represented behaviors to detect VPN servers in real-world scenarios. |
Chenxu Wang; Jiangyi Yin; Zhao Li; Hongbo Xu; Zhongyi Zhang; Qingyun Liu; |
| 213 | PhishinWebView: Analysis of Anti-Phishing Entities in Mobile Apps with WebView Targeted Phishing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on our experiment results, we present security recommendations to take proactive phishing cautions using link preview bots. |
Yoonjung Choi; Woonghee Lee; Junbeom Hur; |
| 214 | Modularized Networks for Few-shot Hateful Meme Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we address the challenge of detecting hateful memes in the low-resource setting where only a few labeled examples are available. |
Rui Cao; Roy Ka-Wei Lee; Jing Jiang; |
| 215 | When Imbalance Meets Imbalance: Structure-driven Learning for Imbalanced Graph Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a carefully designed structure-driven learning framework called ImbGNN to address the potential intertwined class imbalance and structural imbalance in graph classification. |
Wei Xu; Pengkun Wang; Zhe Zhao; Binwu Wang; Xu Wang; Yang Wang; |
| 216 | Densest Subhypergraph: Negative Supermodular Functions and Strongly Localized Methods Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present several contributions for localized densest subgraph discovery, which seeks dense subgraphs located nearby given seed sets of nodes. |
Yufan Huang; David F. Gleich; Nate Veldt; |
| 217 | Negative Sampling in Next-POI Recommendations: Observation, Approach, and Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To recommend the points of interest (POIs) that a user would check-in next, most deep-learning (DL)-based existing studies have employed random negative (RN) sampling during model training. In this paper, we claim and validate that, as the training proceeds, such an RN sampling in reality performs as sampling easy negative (EN) POIs (i.e., EN sampling) that a user was highly unlikely to check-in at her check-in time point. |
Hong-Kyun Bae; Yebeen Kim; Hyunjoon Kim; Sang-Wook Kim; |
| 218 | DirectFaaS: A Clean-Slate Network Architecture for Efficient Serverless Chain Communications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose DirectFaaS, a clean-slate network architecture to improve the function chain communication performance. |
Qingyang Zeng; Kaiyu Hou; Xue Leng; Yan Chen; |
| 219 | Link Recommendation to Augment Influence Diffusion with Provable Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an algorithm, namely AIS, consisting of an efficient estimator for augmented influence estimation and an accelerated sampling approach. |
Xiaolong Chen; Yifan Song; Jing Tang; |
| 220 | Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods fail to model the continuous dynamic effects of treatments on the outcome, especially when multiple treatments (e.g., stay-at-home and get-vaccine policies) are applied simultaneously. To tackle this challenge, we propose Causal Graph Ordinary Differential Equations (CAG-ODE), a novel model that captures the continuous interaction among agents using a Graph Neural Network (GNN) as the ODE function. |
Zijie Huang; Jeehyun Hwang; Junkai Zhang; Jinwoo Baik; Weitong Zhang; Dominik Wodarz; Yizhou Sun; Quanquan Gu; Wei Wang; |
| 221 | ReliK: A Reliability Measure for Knowledge Graph Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the problem of anticipating the performance of a given KGE in a certain downstream task and locally to a specific individual triple, has not been tackled so far.In this paper, we fill this gap withReliK, a Reli ability measure for K GEs. ReliK relies solely on KGE embedding scores, is task- and KGE-agnostic, and requires no further KGE training. |
Maximilian K. Egger; Wenyue Ma; Davide Mottin; Panagiotis Karras; Ilaria Bordino; Francesco Gullo; Aris Anagnostopoulos; |
| 222 | Understanding Human Preferences: Towards More Personalized Video to Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Different from user modeling in collaborative filtering, there is no other user behaviors in inference as a real-time video stream is coming. In this paper, we formally define the task of personalized video commenting task and design an end-to-end personalized framework for solving this task. |
Yihan Wu; Ruihua Song; Xu Chen; Hao Jiang; Zhao Cao; Jin Yu; |
| 223 | Invariant Graph Learning for Causal Effect Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they mainly focus on transductive causal effect learning on a single networked data, limiting their efficacy in inductive settings for real-world applications where networked data often originates from multiple environments influenced by potentially varying time or geographical regions. In light of this, we introduce the principle of invariance to the task of causal effect estimation on networked data, culminating in our Invariant Graph Learning (IGL) framework. |
Yongduo Sui; Caizhi Tang; Zhixuan Chu; Junfeng Fang; Yuan Gao; Qing Cui; Longfei Li; Jun Zhou; Xiang Wang; |
| 224 | Intersectional Two-sided Fairness in Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we argue that the intersectional two-sided unfairness may still exist even if the RS is two-sided fair, which is observed and shown by empirical studies on real-world data in this paper, and has not been well-studied previously. To mitigate this problem, we propose a novel approach called Intersectional Two-sided Fairness Recommendation (ITFR). |
Yifan Wang; Peijie Sun; Weizhi Ma; Min Zhang; Yuan Zhang; Peng Jiang; Shaoping Ma; |
| 225 | Characterizing Ethereum Upgradable Smart Contracts and Their Security Implications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct a large-scale measurement study to characterize USCs and their security implications in the wild. |
Xiaofan Li; Jin Yang; Jiaqi Chen; Yuzhe Tang; Xing Gao; |
| 226 | Adversarial-Enhanced Causal Multi-Task Framework for Debiasing Post-Click Conversion Rate Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, inaccuracies in imputed errors can increase the uncertainty in the generalization bound of CVR predictions, consequently resulting in a decline in the CVR prediction accuracy. To challenge this issue, we first present a theoretical analysis of the bias and variance inherent in DR estimators and then introduce a novel causal estimator that seeks to strike a balance between bias and variance within the DR framework, thus optimizing the learning of the imputation model in a more robust manner. |
Xinyue Zhang; Cong Huang; Kun Zheng; Hongzu Su; Tianxu Ji; Wei Wang; Hongkai Qi; Jingjing Li; |
| 227 | M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, through causal graph analysis, we have discovered that the scenario itself directly influences click behavior, yet existing approaches directly incorporate data from other scenarios during the training of the current scenario, leading to prediction biases when they directly utilize click behaviors from other scenarios to train models. To address these problems, we propose the Multi-Scenario Causal-driven Adaptive Network M-scan). |
Jiachen Zhu; Yichao Wang; Jianghao Lin; Jiarui Qin; Ruiming Tang; Weinan Zhang; Yong Yu; |
| 228 | Link Prediction on Multilayer Networks Through Learning of Within-Layer and Across-Layer Node-Pair Structural Features and Node Embedding Similarity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to fill a lack of multilayer graph representation learning methods designed for link prediction. |
Lorenzo Zangari; Domenico Mandaglio; Andrea Tagarelli; |
| 229 | Poisoning Attack on Federated Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper is the first work to systematize the risks of FKGE poisoning attacks, from which we develop a novel framework for poisoning attacks that force the victim client to predict specific false facts. |
Enyuan Zhou; Song Guo; Zhixiu Ma; Zicong Hong; Tao Guo; Peiran Dong; |
| 230 | Macro Graph Neural Networks for Online Billion-Scale Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, sampling only a small portion of neighbors results in a severe sampling bias and the failure to encompass the full spectrum of user or item behavioral patterns. To address this challenge, we name the conventional user-item recommendation graph as micro recommendation grap and introduce a revolutionizing MAcro Recommendation Graph (MAG) for billion-scale recommendations to reduce the neighbor count from billions to hundreds in the graph structure infrastructure. |
Hao Chen; Yuanchen Bei; Qijie Shen; Yue Xu; Sheng Zhou; Wenbing Huang; Feiran Huang; Senzhang Wang; Xiao Huang; |
| 231 | Masked Graph Autoencoder with Non-discrete Bandwidths Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we unveil that the existing discrete edge masking and binary link reconstruction strategies are insufficient to learn topologically informative representations, from the perspective of message propagation on graph neural networks. |
Ziwen Zhao; Yuhua Li; Yixiong Zou; Jiliang Tang; Ruixuan Li; |
| 232 | WEFix: Intelligent Automatic Generation of Explicit Waits for Efficient Web End-to-End Flaky Tests Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose WEFix, a technique that can automatically generate fixes for UI-based flakiness in web e2e testing. |
Xinyue Liu; Zihe Song; Weike Fang; Wei Yang; Weihang Wang; |
| 233 | Unlocking The Non-deterministic Computing Power with Memory-Elastic Multi-Exit Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MEEdge, a system that automatically transforms classic single-exit models into heterogeneous and dynamic multi-exit models which enables Memory-Elastic inference at the Edge with non-deterministic computing power. |
Jiaming Huang; Yi Gao; Wei Dong; |
| 234 | Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a time-increasing bandit algorithm TI-UCB, which effectively predicts the increase of model performances due to training or finetuning and efficiently balances exploration and exploitation in model selection. |
Yu Xia; Fang Kong; Tong Yu; Liya Guo; Ryan A. Rossi; Sungchul Kim; Shuai Li; |
| 235 | Are Adversarial Phishing Webpages A Threat in Reality? Understanding The Users’ Perception of Adversarial Webpages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing works mostly focused on assessing adversarial phishing webpages against ML-PWD, while neglecting a crucial aspect: investigating whether they can deceive the actual target of phishing—the end users. In this paper, we fill this gap by conducting two user studies (n=470) to examine how human users perceive adversarial phishing webpages, spanning both synthetically crafted ones (which we create by evading a state-of-the-art ML-PWD) as well as real adversarial webpages (taken from the wild Web) that bypassed a production-grade ML-PWD. |
Ying Yuan; Qingying Hao; Giovanni Apruzzese; Mauro Conti; Gang Wang; |
| 236 | Heterogeneous Subgraph Transformer for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To bridge the gap, this work proposes a heterogeneous subgraph transformer HeteroSGT to exploit subgraphs in our constructed heterogeneous graph. |
Yuchen Zhang; Xiaoxiao Ma; Jia Wu; Jian Yang; Hao Fan; |
| 237 | Social Media Discourses on Interracial Intimacy: Tracking Racism and Sexism Through Chinese Geo-located Social Media Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We examine the social media discourse surrounding interracial relationships in China, specifically on the popular platform Douyin. |
Zheng Wei; Yixuan Xie; Danyun Xiao; Simin Zhang; Pan Hui; Muzhi Zhou; |
| 238 | Understanding GDPR Non-Compliance in Privacy Policies of Alexa Skills in European Marketplaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Skills that involve data collection should provide a privacy policy to disclose the data practice to users and meet GDPR requirements.In this work, we analyze the privacy policies of skills in European marketplaces, focusing on whether skills’ privacy policies and data collection behaviors comply with GDPR. |
Song Liao; Mohammed Aldeen; Jingwen Yan; Long Cheng; Xiapu Luo; Haipeng Cai; Hongxin Hu; |
| 239 | Author Name Disambiguation Via Paper Association Refinement and Compositional Contrastive Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: First, the heuristically constructed paper association graphs used for representation learning contain uncertainties that may cause negative supervision. Second, existing algorithms, such as binary cross-entropy loss, used to train representation learning models may not produce sufficiently high-quality representations for AND. To tackle the above problems, we propose an association refining and compositional contrasting (ARCC) framework for AND tasks. ARCC first adopts an iterative graph structure refinement process to dynamically reduce the uncertainties in paper graphs. |
Dezhi Liu; Richong Zhang; Junfan Chen; Xinyue Chen; |
| 240 | DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a GCL model named DSLR, specifically, we devise a coverage-based diversity (CD) approach to consider both the class representativeness and the diversity within each class of the replayed nodes. |
Seungyoon Choi; Wonjoong Kim; Sungwon Kim; Yeonjun In; Sein Kim; Chanyoung Park; |
| 241 | UniLP: Unified Topology-aware Generative Framework for Link Prediction in Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, unifying all subtasks in LP presents numerous challenges, including unified input forms, task-specific context modeling, and topological information encoding. To address these challenges, we propose a topology-aware generative framework, namely UniLP, which utilizes a generative pre-trained language model to accomplish different LP subtasks universally. |
Ben Liu; Miao Peng; Wenjie Xu; Xu Jia; Min Peng; |
| 242 | MemeCraft: Contextual and Stance-Driven Multimodal Meme Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the development of several meme generation tools, there remains a gap in their systematic evaluation and their ability to effectively communicate ideologies. Addressing this, we introduce MemeCraft, an innovative meme generator that leverages large language models (LLMs) and visual language models (VLMs) to produce memes advocating specific social movements. |
Han Wang; Roy Ka-Wei Lee; |
| 243 | (In)Security of File Uploads in Node.js Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we analyze the (in)security of popular file upload libraries and real-world applications in the Node.js ecosystem. |
Harun Oz; Abbas Acar; Ahmet Aris; G\{u}liz Seray Tuncay; Amin Kharraz; Selcuk Uluagac; |
| 244 | Rethinking Node-wise Propagation for Large-scale Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Intuitively, different nodes in web-scale graphs possess distinct topological roles, and therefore propagating them indiscriminately or neglecting local contexts may compromise the quality of node representations. To address the above issues, we propose Adaptive Topology-aware Propagation (ATP), which reduces potential high-bias propagation and extracts structural patterns of each node in a scalable manner to improve running efficiency and predictive performance. |
Xunkai Li; Jingyuan Ma; Zhengyu Wu; Daohan Su; Wentao Zhang; Rong-Hua Li; Guoren Wang; |
| 245 | Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Subsequently, we propose AdaptKry, an optimized polynomial graph filter utilizing bases from the adaptive Krylov subspaces. |
Keke Huang; Wencai Cao; Hoang Ta; Xiaokui Xiao; Pietro Li\`{o}; |
| 246 | Mechanism Design for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We argue that this problem, while generally falling under the umbrella of mechanism design, has several unique features. We propose a general formalism—the token auction model—for studying this problem. |
Paul D\{u}tting; Vahab Mirrokni; Renato Paes Leme; Haifeng Xu; Song Zuo; |
| 247 | Towards Explainable Harmful Meme Detection Through Multimodal Debate Between Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an explainable approach to detect harmful memes, achieved through reasoning over conflicting rationales from both harmless and harmful positions. |
Hongzhan Lin; Ziyang Luo; Wei Gao; Jing Ma; Bo Wang; Ruichao Yang; |
| 248 | Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper shares lessons learned regarding the challenges of naively using AED systems in industrial settings where non-stationarity is prevalent, while also providing perspectives on the proper objectives and system specifications in such settings. We developed an AED framework for counterfactual inference based on these experiences, and tested it in a commercial environment. |
Tanner Fiez; Houssam Nassif; Yu-Cheng Chen; Sergio Gamez; Lalit Jain; |
| 249 | Stable-Sketch: A Versatile Sketch for Accurate, Fast, Web-Scale Data Stream Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In effect, items of interest that are tracked only based on their features (e.g., item frequency or persistence value) are susceptible to replacement by non-relevant ones, leading to modest detection accuracy, as we reveal. In this work, we introduce the notion of bucket stability, which quantifies the degree of recorded item variation, and show that this is a powerful metric for identifying distinct item types. |
Weihe Li; Paul Patras; |
| 250 | Bayesian Iterative Prediction and Lexical-based Interpretation for Disturbed Chinese Sentence Pair Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a series of model-agnostic techniques aimed at enhancing both the accuracy and interpretability of Chinese pairwise sentence-matching models. |
Muzhe Guo; Muhao Guo; Juntao Su; Junyu Chen; Jiaqian Yu; Jiaqi Wang; Hongfei Du; Parmanand Sahu; Ashwin Assysh Sharma; Fang Jin; |
| 251 | Accelerating The Decentralized Federated Learning Via Manipulating Edges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, DFL faces challenges in terms of slow convergence rate due to complex P2P graphs. To address this issue, we propose an efficient algorithm to accelerate DFL by introducing a limited number of k of edges into the P2P graphs. |
Mingyang Zhou; Gang Liu; KeZhong Lu; Rui Mao; Hao Liao; |
| 252 | Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose confined gradient descent (CGD) that enhances the privacy of federated learning by eliminating the sharing of global model parameters. |
Yanjun Zhang; Ruoxi Sun; Liyue Shen; Guangdong Bai; Minhui Xue; Mark Huasong Meng; Xue Li; Ryan Ko; Surya Nepal; |
| 253 | Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To enable a computationally efficient exposure control even for such large-scale systems, this work develops a scalable, fast, and fair method called exposure-aware ADMM (exADMM ). |
Riku Togashi; Kenshi Abe; Yuta Saito; |
| 254 | Top-Personalized-K Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We point out that providing fixed-size recommendations without taking into account user utility can be suboptimal, as it may unavoidably include irrelevant items or limit the exposure to relevant ones. To address this issue, we introduce Top-Personalized-K Recommendation, a new recommendation task aimed at generating a personalized-sized ranking list to maximize individual user satisfaction. |
Wonbin Kweon; SeongKu Kang; Sanghwan Jang; Hwanjo Yu; |
| 255 | Doubly Calibrated Estimator for Recommendation on Data Missing Not at Random Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We provide theoretical insights into how miscalibrated imputation and propensity models may limit the effectiveness of doubly robust estimators and validate our theorems using real-world datasets. On this basis, we propose a Doubly Calibrated Estimator that involves the calibration of both the imputation and propensity models. |
Wonbin Kweon; Hwanjo Yu; |
| 256 | Detecting Illicit Food Factories from Chemical Declaration Data Via Graph-aware Self-supervised Contrastive Anomaly Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the global food industry, where the line between legitimate and illicit manufacturing is increasingly blurred by the scale and complexity of the supply chain, safeguarding consumer health and trust necessitates innovative detection methods. Addressing this, this paper presents Graph-aware Self-supervised Contrastive Anomaly Ranking (GraphCAR), a novel unsupervised learning model, devised to identify illicit food factories through the scrutiny of chemical declaration data. |
Sheng-Fang Yang; Cheng-Te Li; |
| 257 | Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models. |
Liyang He; Zhenya Huang; Jiayu Liu; Enhong Chen; Fei Wang; Jing Sha; Shijin Wang; |
| 258 | NCTM: A Novel Coded Transmission Mechanism for Short Video Deliveries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to reduce the amount of traffic, we design a Novel Coded Transmission Mechanism (NCTM), which transmits XOR-coded data instead of the original video content. |
Zhenge Xu; Qing Li; Wanxin Shi; Yong Jiang; Zhenhui Yuan; Peng Zhang; Gabriel-Miro Muntean; |
| 259 | SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although there are effective spatio-temporal inference methods, they face challenges such as scalability with large datasets and inadequate semantic understanding, which impede their performance. To address these limitations, this paper introduces a novel framework – Simple Spatio-Temporal Knowledge Graph (SSTKG), for constructing and exploring spatio-temporal KGs. |
Ruiyi Yang; Flora D. Salim; Hao Xue; |
| 260 | Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study the performance of EA methods on the alignment of highly heterogeneous KGs (HHKGs). |
Xuhui Jiang; Chengjin Xu; Yinghan Shen; Yuanzhuo Wang; Fenglong Su; Zhichao Shi; Fei Sun; Zixuan Li; Jian Guo; Huawei Shen; |
| 261 | DRAM-like Architecture with Asynchronous Refreshing for Continual Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches in this field predominantly rely on memory-based methods to alleviate catastrophic forgetting, which overlooks the inherent challenge posed by the varying memory requirements of different relations and the need for a suitable memory refreshing strategy. Drawing inspiration from the mechanisms of Dynamic Random Access Memory (DRAM), our study introduces a novel CRE architecture with an asynchronous refreshing strategy to tackle these challenges. |
Tianci Bu; Kang Yang; Wenchuan Yang; Jiawei Feng; Xiaoyu Zhang; Xin Lu; |
| 262 | Fast Inference of Removal-Based Node Influence Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an efficient, intuitive, and effective method, NOde-Removal-based fAst GNN inference (NORA), which uses the gradient information to approximate the node-removal influence. |
Weikai Li; Zhiping Xiao; Xiao Luo; Yizhou Sun; |
| 263 | AdFlush: A Real-World Deployable Machine Learning Solution for Effective Advertisement and Web Tracker Prevention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces AdFlush, a novel machine learning model for real-world browsers. |
Kiho Lee; Chaejin Lim; Beomjin Jin; Taeyoung Kim; Hyoungshick Kim; |
| 264 | Euphemism Identification Via Feature Fusion and Individualization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, we observe that different euphemisms in similar contexts confuse the identification results. To overcome these obstacles, we propose a feature fusion and individualization (FFI) method for euphemism identification. |
Yuxue Hu; Mingmin Wu; Zhongqiang Huang; Junsong Li; Xing Ge; Ying Sha; |
| 265 | Individual Welfare Guarantees in The Autobidding World with Machine-learned Advice Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we demonstrate how ad platforms can utilize ML advice to improve welfare guarantee on the aggregate and individual bidder level by setting ML advice as personalized reserve prices when the platform consists ofautobidders who maximize value while respecting a return on ad spend (ROAS) constraint. Under parallel VCG auctions with such ML advice-based reserves, we present a worst-case welfare lower-bound guarantee for an individual autobidder, and show that the lower-bound guarantee is positively correlated with ML advice quality as well as the scale of bids induced by the autobidder’s bidding strategies. |
Yuan Deng; Negin Golrezaei; Patrick Jaillet; Jason Cheuk Nam Liang; Vahab Mirrokni; |
| 266 | Uncovering The Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we investigate the deep filter bubble, which refers to the user being exposed to narrow content within their broad interests. |
Nicholas Sukiennik; Chen Gao; Nian Li; |
| 267 | Temporal Conformity-aware Hawkes Graph Network for Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we identify two distinct types of conformity behavior: informational conformity and normative conformity. |
Chenglong Ma; Yongli Ren; Pablo Castells; Mark Sanderson; |
| 268 | AN-Net: An Anti-Noise Network for Anonymous Traffic Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Anti-Noise Network (AN-Net) to construct robust short-term representations for a single modality, effectively countering irrelevant packet noise. |
Xianwen Deng; Yijun Wang; Zhi Xue; |
| 269 | Jointly Canonicalizing and Linking Open Knowledge Base Via Unified Embedding Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although these two tasks are inherently complementary with each other, previous studies just solve them separately or via superficial interaction. To address this issue, we propose CLUE, a novel framework that jointly encodes the OKB and CKB into a unified embedding space, to tackle OKB canonicalization and OKB linking simultaneously and make them benefit each other reciprocally. |
Wei Shen; Binhan Yang; Yinan Liu; |
| 270 | FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: How to ensure group exposures in the distributed retrieval process is a challenging question. To address this issue, we introduce a model named FairSync, which transforms the problem into a constrained distributed optimization problem. |
Chen Xu; Jun Xu; Yiming Ding; Xiao Zhang; Qi Qi; |
| 271 | Navigating The Post-API Dilemma Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the present work we ask: does SERP provide a complete and unbiased sample of social media data? |
Amrit Poudel; Tim Weninger; |
| 272 | Optimizing Network Resilience Via Vertex Anchoring Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose and study thefollower maximization problem: maximizing the resilience gain (the number of coreness-increased vertices) via anchoring a set of vertices within a given budget. |
Siyi Teng; Jiadong Xie; Fan Zhang; Can Lu; Juntao Fang; Kai Wang; |
| 273 | Exploring Unconfirmed Transactions for Effective Bitcoin Address Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we bridge the gap by combining confirmed and unconfirmed transactions for effective Bitcoin address clustering. |
Kai Wang; Yakun Cheng; Michael Wen Tong; Zhenghao Niu; Jun Pang; Weili Han; |
| 274 | SatGuard: Concealing Endless and Bursty Packet Losses in LEO Satellite Networks for Delay-Sensitive Web Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose SatGuard, a distributed in-orbit loss recovery mechanism that can reduce user-perceived delay by completely concealing packet losses in the unstable and lossy LSN environment from endpoints. |
Jihao Li; Hewu Li; Zeqi Lai; Qian Wu; Yijie Liu; Qi Zhang; Yuanjie Li; Jun Liu; |
| 275 | Federated Learning Vulnerabilities: Privacy Attacks with Denoising Diffusion Probabilistic Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Gradient Guided Diffusion Model (GGDM), a novel learning-free approach based on a pre-trained unconditional Denoising Diffusion Probabilistic Models (DDPM), aimed at improving the effectiveness and reducing the difficulty of implementing gradient based privacy attacks on complex networks and high-resolution images. |
Hongyan Gu; Xinyi Zhang; Jiang Li; Hui Wei; Baiqi Li; Xinli Huang; |
| 276 | Entity Disambiguation with Extreme Multi-label Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although many recent studies apply deep learning to achieve decent results, they need exhausting pre-training and mediocre recall in the retrieval stage. In this paper, we propose a novel framework, eXtreme Multi-label Ranking for Entity Disambiguation (XMRED), to address this challenge. |
Jyun-Yu Jiang; Wei-Cheng Chang; Jiong Zhang; Cho-Jui Hsieh; Hsiang-Fu Yu; |
| 277 | Fair Surveillance Assignment Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, each agent’s patrolling cost towards receiving a subgraph is measured by the weight of the minimum vertex cover therein, and our objective is to design algorithms to compute fair assignments of the surveillance tasks. |
Fangxiao Wang; Bo Li; |
| 278 | ESCNet: Entity-enhanced and Stance Checking Network for Multi-modal Fact-Checking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To establish baseline performance, we propose a novel Entity-enhanced and Stance Checking Network (ESCNet), which includes Multi-modal Feature Extraction Module, Stance Transformer, and Entity-enhanced Encoder. |
Fanrui Zhang; Jiawei Liu; Jingyi Xie; Qiang Zhang; Yongchao Xu; Zheng-Jun Zha; |
| 279 | Exit Ripple Effects: Understanding The Disruption of Socialization Networks Following Employee Departures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Using rich communication data from a large holding company, we examine the effects of employee departures on socialization networks among the remaining coworkers. |
David Gamba; Yulin Yu; Yuan Yuan; Grant Schoenebeck; Daniel M. Romero; |
| 280 | OODREB: Benchmarking State-of-the-Art Methods for Out-Of-Distribution Generalization on Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we serve as the first effort to study out-of-distribution (OOD) problems in RE by constructing an out-of-distribution relation extraction benchmark (OODREB) and then investigating the abilities of state-of-the-art (SOTA) RE methods on OODREB in both i.i.d. and OOD settings. |
Haotian Chen; Houjing Guo; Bingsheng Chen; Xiangdong Zhou; |
| 281 | MSynFD: Multi-hop Syntax Aware Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These existing methods fail to handle the complex, subtle twists1 in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing. To bridge these significant gaps, we propose a novel multi-hop syntax aware fake news detection (MSynFD) method, which incorporates complementary syntax information to deal with subtle twists in fake news. |
Liang Xiao; Qi Zhang; Chongyang Shi; Shoujin Wang; Usman Naseem; Liang Hu; |
| 282 | MMAdapt: A Knowledge-guided Multi-source Multi-class Domain Adaptive Framework for Early Health Misinformation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a multi-source domain adaptive framework that jointly exploits medical knowledge and annotated data from different high-resource source domains (e.g., cancer, COVID-19) to detect misleading posts in an emergent target domain (e.g., mpox, polio). |
Lanyu Shang; Yang Zhang; Bozhang Chen; Ruohan Zong; Zhenrui Yue; Huimin Zeng; Na Wei; Dong Wang; |
| 283 | Cold Start or Hot Start? Robust Slow Start in Congestion Control with A Priori Knowledge for Mobile Web Services Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose WiseStart, a hot-start-based slow start mechanism. |
Jia Zhang; Haixuan Tong; Enhuan Dong; Xin Qian; Mingwei Xu; Xiaotian Li; Zili Meng; |
| 284 | Fact Embedding Through Diffusion Model for Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper argues that plausible facts in a knowledge graph come from a distribution in the low-dimensional fact space. Inspired by this insight, this paper proposes a novel framework called Fact Embedding through Diffusion Model (FDM) to address the knowledge graph completion task. |
Xiao Long; Liansheng Zhuang; Aodi Li; Houqiang Li; Shafei Wang; |
| 285 | FusionRender: Harnessing WebGPU’s Power for Enhanced Graphics Performance on Web Browsers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enhance the graphics performance on the web, we introduce the FusionRender to harness the power of WebGPU. |
Weichen Bi; Yun Ma; Yudong Han; Yifan Chen; Deyu Tian; Jiaqi Du; |
| 286 | ContraMTD: An Unsupervised Malicious Network Traffic Detection Method Based on Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method ContraMTD based on contrastive learning, which comprehensively considers both vertical and horizontal perspectives. |
Xueying Han; Susu Cui; Jian Qin; Song Liu; Bo Jiang; Cong Dong; Zhigang Lu; Baoxu Liu; |
| 287 | Low Mileage, High Fidelity: Evaluating Hypergraph Expansion Methods By Quantifying The Information Loss Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first define information loss that occurs in the hypergraph expansion and then propose a novel framework, named MILEAGE, to evaluate hypergraph expansion methods by measuring their degree of information loss. |
David Y. Kang; Qiaozhu Mei; Sang-Wook Kim; |
| 288 | Distributionally Robust Graph-based Recommendation System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite its significance, researches on GNN-based recommendation against distribution shift are still sparse. To bridge this gap, we propose Distributionally Robust GNN (DR-GNN) that incorporates Distributional Robust Optimization (DRO) into the GNN-based recommendation. |
Bohao Wang; Jiawei Chen; Changdong Li; Sheng Zhou; Qihao Shi; Yang Gao; Yan Feng; Chun Chen; Can Wang; |
| 289 | Matching Feature Separation Network for Domain Adaptation in Entity Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, apply-ing DL-based EM methods often costs a lot of human efforts to label the data. To address this challenge, we propose a new do-main adaptation (DA) framework for EM called Matching Fea-ture Separation Network (MFSN). |
Chenchen Sun; Yang Xu; Derong Shen; Tiezheng Nie; |
| 290 | Experimental Security Analysis of Sensitive Data Access By Browser Extensions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present an empirical study of the security risks posed by browser extensions. |
Asmit Nayak; Rishabh Khandelwal; Earlence Fernandes; Kassem Fawaz; |
| 291 | Contrastive Learning for Multimodal Classification of Crisis Related Tweets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the effectiveness of pre-trained multimodal contrastive learning models, specifically, CLIP, and ALIGN, on the task of classifying multimodal crisis related tweets. |
Bishwas Mandal; Sarthak Khanal; Doina Caragea; |
| 292 | SceneDAPR: A Scene-Level Free-Hand Drawing Dataset for Web-based Psychological Drawing Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the interpretation of a large number of drawing assessments solely relies on human experts, requiring much time and cost. To address this issue, we introduce a novel scene-level sketch dataset, SceneDAPR, which can be used to automatically analyze the drawing assessment, Draw-A-Person-in-the-Rain (DAPR), a popular psychological drawing assessment used for identifying stressful experiences and coping behavior. |
Jiwon Kang; Jiwon Kim; Migyeong Yang; Chaehee Park; Taeeun Kim; Hayeon Song; Jinyoung Han; |
| 293 | Meet Challenges of RTT Jitter, A Hybrid Internet Congestion Control Algorithm Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we model it as a normal distribution based on our measurements and propose a novel congestion control algorithm LingBo. |
Lianchen Jia; Chao Zhou; Tianchi Huang; Chaoyang Li; Lifeng Sun; |
| 294 | Core-Competitiveness in Partially Observable Networked Market Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Governed by this impossible result, we identify the criteria that the allocation rule for PONM should meet. Based on these criteria, we propose a new class of auction mechanisms for PONM that is individually rational, incentive-compatible, and core-competitive. |
Bin Li; Dong Hao; |
| 295 | ARES: Predictable Traffic Engineering Under Controller Failures in SD-WANs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ARES to provide predictable TE performance under controller failures. |
Songshi Dou; Li Qi; Zehua Guo; |
| 296 | Graph Contrastive Learning Via Interventional View Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, the efficacy of GCL could greatly deteriorate on heterophilic graphs, verified by our analysis: GCL on a mixture of homophilic and heterophilic edges will generate representations that are indistinguishable across different classes in the embedding space. To address this challenge, we propose a novel GCL framework via interventional view generation. |
Zengyi Wo; Minglai Shao; Wenjun Wang; Xuan Guo; Lu Lin; |
| 297 | Not All Asians Are The Same: A Disaggregated Approach to Identifying Anti-Asian Racism in Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By introducing several techniques that facilitate comparisons of online anti-Asian hate towards diverse ethnic communities, this study highlights the importance of taking a nuanced and disaggregated approach for understanding racial hatred to formulate effective mitigation strategies. |
Fan Wu; Sanyam Lakhanpal; Qian Li; Kookjin Lee; Doowon Kim; Heewon Chae; Kyounghee Hazel Kwon; |
| 298 | Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The trained policy can subsequently be deployed for further data collection, resulting in an iterative training framework, which we refer to as iterative offline RL. In this work, we identify the performance bottleneck of this iterative offline RL framework, which originates from the ineffective exploration and exploitation caused by the inherent conservatism of offline RL algorithms. |
Haoming Li; Yusen Huo; Shuai Dou; Zhenzhe Zheng; Zhilin Zhang; Chuan Yu; Jian Xu; Fan Wu; |
| 299 | Robust Route Planning Under Uncertain Pickup Requests for Last-mile Delivery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we take conformal prediction as an opportunity to address the issue of prediction uncertainty. |
Hua Yan; Heng Tan; Haotian Wang; Desheng Zhang; Yu Yang; |
| 300 | Reconciling The Accuracy-Diversity Trade-off in Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In a theoretical model, we show that utility-maximizing recommendations—when accounting for consumption constraints—are naturally diverse due to diminishing returns of recommending similar items. |
Kenny Peng; Manish Raghavan; Emma Pierson; Jon Kleinberg; Nikhil Garg; |
| 301 | Efficient Computation for Diagonal of Forest Matrix Via Variance-Reduced Forest Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome the issue, in this paper, we propose three novel sampling-based algorithms:SCF,SCFV,and SCFV+. |
Haoxin Sun; Zhongzhi Zhang; |
| 302 | Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, many existing diversity-focused methods fail to leverage crucial item information, such as item category and popularity during neighbor modeling and message propagation. To address these challenges, we introduce a novel framework, called CPGRec, comprising three modules, namely accuracy-driven, diversity-driven, and comprehensive modules. |
Xiping Li; Jianghong Ma; Kangzhe Liu; Shanshan Feng; Haijun Zhang; Yutong Wang; |
| 303 | HetGPT: Harnessing The Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing graph prompting techniques are tailored to homogeneous graphs, neglecting the inherent heterogeneity of Web graphs. To bridge this gap, we propose HetGPT, a general post-training prompting framework to improve the predictive performance of pre-trained heterogeneous graph neural networks (HGNNs). |
Yihong Ma; Ning Yan; Jiayu Li; Masood Mortazavi; Nitesh V. Chawla; |
| 304 | A Study of GDPR Compliance Under The Transparency and Consent Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a study of GDPR compliance under the Interactive Advertising Bureau Europe’s Transparency and Consent Framework (TCF). |
Michael Smith; Antonio Torres-Ag\{u}ero; Riley Grossman; Pritam Sen; Yi Chen; Cristian Borcea; |
| 305 | Budget-Constrained Auctions with Unassured Priors: Strategic Equivalence and Structural Properties Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we examine five budget-constrained parameterized mechanisms: bid-discount/pacing first-price/second-price auctions and the Bayesian revenue-optimal auction. We consider the unassured prior game among the seller and all buyers induced by these five mechanisms in the stochastic model. |
Zhaohua Chen; Mingwei Yang; Chang Wang; Jicheng Li; Zheng Cai; Yukun Ren; Zhihua Zhu; Xiaotie Deng; |
| 306 | Ad Vs Organic: Revisiting Incentive Compatible Mechanism Design in E-commerce Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a necessary property called form stability, and provide simplification results of the mechanism design problem. |
Ningyuan Li; Yunxuan Ma; Yang Zhao; Qian Wang; Zhilin Zhang; Chuan Yu; Jian Xu; Bo Zheng; Xiaotie Deng; |
| 307 | An Efficient Automatic Meta-Path Selection for Social Event Detection Via Hyperbolic Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The challenges that have a greater impact on social media detection models are as follows: (1) the amount of social media data is huge but its availability is small; (2) social media data is a tree structure and traditional Euclidean space embedding will distort embedded features; and (3) the heterogeneity of social media networks makes existing models unable to capture rich information well. To solve the above challenges, we propose a Heterogeneous Information Graph representation via Hyperbolic space combined with an Automatic Meta-path selection (GraphHAM) model, an efficient framework that automatically selects the meta-path’s weight and combines hyperbolic space to learn information on social media. |
Zitai Qiu; Congbo Ma; Jia Wu; Jian Yang; |
| 308 | A Cross Domain Method for Customer Lifetime Value Prediction in Supply Chain Platform Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, the long-tail distribution of customer LTV also brings new challenges to the prediction of LTV. To tackle the above issues, we propose CDLtvS, a novel Cross Domain method for customer Lifetime value prediction in SCP. |
Zhiyuan Zhou; Li Lin; Hai Wang; Xiaolei Zhou; Gong Wei; Shuai Wang; |
| 309 | ?Grapher: A Resource-Efficient Serverless System for GNN Serving Through Graph Sharing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Observing the significant data locality in computation graphs of requests, we propose ?Grapher, a serverless system for GNN serving that achieves resource efficiency through graph sharing and fine-grained resource allocation. |
Haichuan Hu; Fangming Liu; Qiangyu Pei; Yongjie Yuan; Zichen Xu; Lin Wang; |
| 310 | Fair Graph Representation Learning Via Sensitive Attribute Disentanglement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we focus on improving the fairness of GNNs while preserving task-related information and propose a fair GNN framework named FairSAD. |
Yuchang Zhu; Jintang Li; Zibin Zheng; Liang Chen; |
| 311 | Filter Bubble or Homogenization? Disentangling The Long-Term Effects of Recommendations on User Consumption Patterns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our simulations show that traditional recommendation algorithms (based on past behavior) mainly reduce filter bubbles by affecting inter-user diversity without significantly impacting intra-user diversity. Building on these findings, we introduce two new recommendation algorithms that take a more nuanced approach by accounting for both types of diversity. |
Md Sanzeed Anwar; Grant Schoenebeck; Paramveer S. Dhillon; |
| 312 | Towards Expansive and Adaptive Hard Negative Mining: Graph Contrastive Learning Via Subspace Preserving Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Embracing the subspace hypothesis in clustering, we propose a method towards expansive and adaptive hard negative mining, referred to as G raph contR astive leA rning via subsP ace prE serving (GRAPE ). |
Zhezheng Hao; Haonan Xin; Long Wei; Liaoyuan Tang; Rong Wang; Feiping Nie; |
| 313 | Spectral Heterogeneous Graph Convolutions Via Positive Noncommutative Polynomials Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Furthermore, these methods cannot learn arbitrary valid heterogeneous graph filters within the spectral domain, which have limited expressiveness. To tackle these issues, we present a positive spectral heterogeneous graph convolution via positive noncommutative polynomials. |
Mingguo He; Zhewei Wei; Shikun Feng; Zhengjie Huang; Weibin Li; Yu Sun; Dianhai Yu; |
| 314 | Finding Densest Subgraphs with Edge-Color Constraints Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The new problem we address is to find a diverse densest subgraph that fulfills given requirements on the numbers of edges of specific colors. |
Lutz Oettershagen; Honglian Wang; Aristides Gionis; |
| 315 | SSI, from Specifications to Protocol? Formally Verify Security! Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We evaluate a bundle of specifications from the Self-Sovereign Identity (SSI) paradigm to construct an authentication protocol for the Web. |
Christoph H.-J. Braun; Ross Horne; Tobias K\{a}fer; Sjouke Mauw; |
| 316 | Divide, Conquer, and Coalesce: Meta Parallel Graph Neural Network for IoT Intrusion Detection at Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes Meta Parallel Graph Neural Network (MPGNN) to establish a scalable Network Intrusion Detection System (NIDS) for large-scale Internet of Things (IoT) networks. |
Hua Ding; Lixing Chen; Shenghong Li; Yang Bai; Pan Zhou; Zhe Qu; |
| 317 | The Double Edged Sword: Identifying Authentication Pages and Their Fingerprinting Behavior Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To the best of our knowledge, no fingerprinting defenses deployed thus far consider this important distinction when blocking fingerprinting attempts, so they might negatively affect website functionality and security. To address this issue we make three main contributions. First, we introduce a novel machine learning-based method to automatically identify authentication pages (i.e. login and sign-up pages). |
Asuman Senol; Alisha Ukani; Dylan Cutler; Igor Bilogrevic; |
| 318 | NPCS: Native Provenance Computation for SPARQL Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This article proposes NPCS, a Native Provenance Computation approach for SPARQL queries. |
Zubaria Asma; Daniel Hern\'{a}ndez; Luis Gal\'{a}rraga; Giorgos Flouris; Irini Fundulaki; Katja Hose; |
| 319 | Breaking The Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a TSAD framework, Dual-TF, that simultaneously uses both the time and frequency domains while breaking the time-frequency granularity discrepancy. |
Youngeun Nam; Susik Yoon; Yooju Shin; Minyoung Bae; Hwanjun Song; Jae-Gil Lee; Byung Suk Lee; |
| 320 | MMPOI: A Multi-Modal Content-Aware Framework for POI Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel multi-modal content-aware framework for POI recommendation (MMPOI). |
Yang Xu; Gao Cong; Lei Zhu; Lizhen Cui; |
| 321 | GNNFingers: A Fingerprinting Framework for Verifying Ownerships of Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we design a novel and effective ownership verification framework for GNNs called GNNFingers to safeguard the IP of GNNs. |
Xiaoyu You; Youhe Jiang; Jianwei Xu; Mi Zhang; Min Yang; |
| 322 | Interface Illusions: Uncovering The Rise of Visual Scams in Cryptocurrency Wallets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Cryptocurrencies, while revolutionary, have become a magnet for malicious actors. With numerous reports underscoring cyberattacks and scams in this domain, our paper takes the lead in characterizing visual scams associated with cryptocurrency wallets—a fundamental component of Web3. |
Guoyi Ye; Geng Hong; Yuan Zhang; Min Yang; |
| 323 | RecurScan: Detecting Recurring Vulnerabilities in PHP Web Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach, namely RecurScan, which can accurately detect recurring vulnerabilities with resilience to code differences. |
Youkun Shi; Yuan Zhang; Tianhao Bai; Lei Zhang; Xin Tan; Min Yang; |
| 324 | MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: MCFEND, as a benchmark dataset, aims to advance Chinese fake news detection approaches in real-world scenarios. |
Yupeng Li; Haorui He; Jin Bai; Dacheng Wen; |
| 325 | Collaborative Metapath Enhanced Corporate Default Risk Assessment on Heterogeneous Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel Heterogeneous Graph Co-Attention Network for corporate default risk assessment. |
Zheng Zhang; Yingsheng Ji; Jiachen Shen; Yushu Chen; Xi Zhang; Guangwen Yang; |
| 326 | Enhancing Fairness in Meta-learned User Modeling Via Adaptive Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through the theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, in order to mitigate the impact of this factor, we introduce a novel Fairness-aware Adaptive Sampling framework for meTa-learning, abbreviated as FAST. |
Zheng Zhang; Qi Liu; Zirui Hu; Yi Zhan; Zhenya Huang; Weibo Gao; Qingyang Mao; |
| 327 | Improving Item-side Fairness of Multimodal Recommendation Via Modality Debiasing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This results in a serious item-side unfairness issue, i.e., some items with prevailing modality content are over-recommended while a large number of items don’t receive adequate recommendation opportunities, leaving corresponding content providers at great disadvantage. Aiming to eliminate such modality bias and promote item-side fairness, we propose a fairness-aware modality debiasing framework based on counterfactual inference. |
Yu Shang; Chen Gao; Jiansheng Chen; Depeng Jin; Yong Li; |
| 328 | Fake Resume Attacks: Data Poisoning on Online Job Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, first time, we demonstrate the critical vulnerabilities found in the common Human Resources (HR) task of matching job seekers and companies on online job platforms. |
Michiharu Yamashita; Thanh Tran; Dongwon Lee; |
| 329 | IDEA-DAC: Integrity-Driven Editing for Accountable Decentralized Anonymous Credentials Via ZK-JSON Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces IDEA-DAC a paradigm shift from the conventional revoke-and-reissue methods, promoting direct and Integrity-Driven Editing (IDE) for Accountable DACs, which results in better integrity accountability, traceability, and system simplicity. |
Shuhao Zheng; Zonglun Li; Junliang Luo; Ziyue Xin; Xue Liu; |
| 330 | Sublinear-Time Opinion Estimation in The Friedkin–Johnsen Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we show that node opinions and all relevant measures, like polarization and disagreement, can be efficiently approximated in time that is sublinear in the size of the network. |
Stefan Neumann; Yinhao Dong; Pan Peng; |
| 331 | Online Sequential Decision-Making with Unknown Delays Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we introduce a family of Follow the Delayed Regularized Leader algorithms for feedback with full information on the loss function, a family of Delayed Mirror Descent algorithms for feedback with gradient information on the loss function and a family of Simplified Delayed Mirror Descent algorithms for feedback with the value information of the loss function’s gradients at corresponding decision points. |
Ping Wu; Heyan Huang; Zhengyang Liu; |
| 332 | Long-term Off-Policy Evaluation and Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing approaches to this problem either need a restrictive assumption about the short-term outcomes called surrogacy or cannot effectively use short-term outcomes, which is inefficient. Therefore, we propose a new framework called Long-term Off-Policy Evaluation (LOPE), which is based on reward function decomposition. |
Yuta Saito; Himan Abdollahpouri; Jesse Anderton; Ben Carterette; Mounia Lalmas; |
| 333 | What News Do People Get on Social Media? Analyzing Exposure and Consumption of News Through Data Donations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, prior studies have been constrained to considering only fragments of users’ news exposure and reactions. To overcome this obstacle, we present an innovative measurement approach centered on donating personal data for scientific purposes, facilitated through a privacy-preserving tool that captures users’ interactions with news on Facebook. |
Salim Chouaki; Abhijnan Chakraborty; Oana Goga; Savvas Zannettou; |
| 334 | Message Injection Attack on Rumor Detection Under The Black-Box Evasion Setting Using Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we focus on the state-of-the-art rumor detectors, which leverage graph neural network based models to predict whether a post is rumor based on the Message Propagation Tree (MPT), a conversation tree with the post as its root and the replies to the post as the descendants of the root. We propose a novel black-box attack method, HMIA-LLM, against these rumor detectors, which uses the Large Language Model to generate malicious messages and inject them into the targeted MPTs. |
Yifeng Luo; Yupeng Li; Dacheng Wen; Liang Lan; |
| 335 | Air-CAD: Edge-Assisted Multi-Drone Network for Real-time Crowd Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Air-CAD, an edge-assisted multi-drone network that uses air-ground cooperation to achieve fast and accurate CAD. |
Yuanzheng Tan; Qing Li; Junkun Peng; Zhenhui Yuan; Yong Jiang; |
| 336 | Friend or Foe? Mining Suspicious Behavior Via Graph Capsule Infomax Detector Against Fraudsters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel graph self-supervised learning framework, Capsule Graph Infomax (termed CapsGI), to overcome the inconsistency of anomaly detection. |
Xiangping Zheng; Bo Wu; Xun Liang; Wei Li; |
| 337 | Content Moderation and The Formation of Online Communities: A Theoretical Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the impact of content moderation policies in online communities. |
Cynthia Dwork; Chris Hays; Jon Kleinberg; Manish Raghavan; |
| 338 | Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite their effectiveness, CL-based methods encounter a limitation in fully exploiting self-supervision signals for users with limited behavior data, as users with extensive behaviors naturally offer more information. To address this problem, we introduce a novel learning paradigm, named Online Self-Supervised Self-distillation for Sequential Recommendation (S4Rec), effectively bridging the gap between self-supervised learning and self-distillation methods. |
Shaowei Wei; Zhengwei Wu; Xin Li; Qintong Wu; Zhiqiang Zhang; Jun Zhou; Lihong Gu; Jinjie Gu; |
| 339 | Adversarial Mask Explainer for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel framework AMExplainer which leverages the concept of adversarial networks to achieve a dual optimization objective in the target function. |
Wei Zhang; Xiaofan Li; Wolfgang Nejdl; |
| 340 | DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Privacy concerns have also been raised for the trained models which could expose the sensitive information of graphs including both node features and the structure information. In this paper, we aim to achieve node-level differential privacy (DP) for training GNNs so that a node and its edges are protected. |
Qiuchen Zhang; Hong kyu Lee; Jing Ma; Jian Lou; Carl Yang; Li Xiong; |
| 341 | Diffusion-based Negative Sampling on Graphs for Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. To address these issues, we investigate a novel strategy of multi-level negative sampling that enables negative node generation with flexible and controllable hardness” levels from the latent space. |
Trung-Kien Nguyen; Yuan Fang; |
| 342 | Hyperlink Hijacking: Exploiting Erroneous URL Links to Phantom Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, no prior research has been dedicated to situations where the linking errors of web publishers (i.e. developers and content contributors) propagate to users. We hypothesize that these ‘hijackable hyperlinks’ exist in large quantities with the potential to generate substantial traffic. |
Kevin Saric; Felix Savins; Gowri Sankar Ramachandran; Raja Jurdak; Surya Nepal; |
| 343 | Zero-shot Image Classification with Logic Adapter and Rule Prompt Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This is because they solely focus on limited visual-attribute feature alignment. Therefore, we propose a Zero-Shot image Classification with Logic adapter and Rule prompt method called ZSCLR, which utilizes logic adapter and rule prompts to encourage the model to capture discriminative image features and achieve reasoning. |
Dongran Yu; Xueyan Liu; Bo Yang; |
| 344 | Global News Synchrony and Diversity During The Start of The COVID-19 Pandemic Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To enable studies of global news coverage, we develop an efficient computational methodology that comprises three components: (i) a transformer model to estimate multilingual news similarity; (ii) a global event identification system that clusters news based on a similarity network of news articles; and (iii) measures of news synchrony across countries and news diversity within a country, based on country-specific distributions of news coverage of the global events. |
Xi Chen; Scott A. Hale; David Jurgens; Mattia Samory; Ethan Zuckerman; Przemyslaw A. Grabowicz; |
| 345 | Self-Paced Pairwise Representation Learning for Semi-Supervised Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unsolved challenges in SSTC are the overfitting problem caused by the limited labeled data and the mislabeling problem of unlabeled texts. To address these issues, this paper proposes a Self-Paced PairWise representation learning (SPPW) model. |
Junfan Chen; Richong Zhang; Jiarui Wang; Chunming Hu; Yongyi Mao; |
| 346 | DualCL: Principled Supervised Contrastive Learning As Mutual Information Maximization for Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Theoretically motivated by a derived lower bound of mutual information maximization, we propose a dual contrastive learning framework DualCL that satisfies three properties, i.e., parameter-free, augmentation-easy and label-aware. |
Chen Junfan; Richong Zhang; Yaowei Zheng; Qianben Chen; Chunming Hu; Yongyi Mao; |
| 347 | Spot Check Equivalence: An Interpretable Metric for Information Elicitation Mechanisms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, different metrics lead to divergent and even contradictory results in various contexts. In this paper, we harmonize these divergent stories, showing that two of these metrics are actually the same within certain contexts and explain the divergence of the third. |
Shengwei Xu; Yichi Zhang; Paul Resnick; Grant Schoenebeck; |
| 348 | Query in Your Tongue: Reinforce Large Language Models with Retrievers for Cross-lingual Search Generative Experience Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces the Multilingual Information Model for Intelligent Retrieval (MIMIR). |
Ping Guo; Yue Hu; Yanan Cao; Yubing Ren; Yunpeng Li; Heyan Huang; |
| 349 | Beyond Labels and Topics: Discovering Causal Relationships in Neural Topic Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on uncovering possible causal relationships both between and within the supervised information and latent topics to better understand the mechanisms behind the emergence of the topics and the labels. |
Yi-Kun Tang; Heyan Huang; Xuewen Shi; Xian-Ling Mao; |
| 350 | ARTEMIS: Detecting Airdrop Hunters in NFT Markets with A Graph Learning System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce ARTEMIS, an optimized graph neural network system for identifying airdrop hunters in NFT transactions. |
Chenyu Zhou; Hongzhou Chen; Hao Wu; Junyu Zhang; Wei Cai; |
| 351 | On Truthful Item-Acquiring Mechanisms for Reward Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we study the problem that a collector acquires items from the owner based on the item qualities the owner declares and an independent appraiser’s assessments. |
Liang Shan; Shuo Zhang; Jie Zhang; Zihe Wang; |
| 352 | MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts Via Automating Deep Neural Network Porting for Mobile Deployment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MatchNAS, a novel scheme for porting DNNs to mobile devices. |
Hongtao Huang; Xiaojun Chang; Wen Hu; Lina Yao; |
| 353 | Predicting and Presenting Task Difficulty for Crowdsourcing Food Rescue Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a hybrid model with tabular and natural language data to predict the difficulty of a given rescue trip, which significantly outperforms baselines in identifying easy and hard rescues. |
Zheyuan Ryan Shi; Jiayin Zhi; Siqi Zeng; Zhicheng Zhang; Ameesh Kapoor; Sean Hudson; Hong Shen; Fei Fang; |
| 354 | MileCut: A Multi-view Truncation Framework for Legal Case Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a multi-view truncation framework for legal case retrieval, named MileCut. |
Fuda Ye; Shuangyin Li; |
| 355 | From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query. |
Philipp Seifer; Daniel Hern\'{a}ndez; Ralf L\{a}mmel; Steffen Staab; |
| 356 | Faithful Temporal Question Answering Over Heterogeneous Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As implicit questions are sparse in prior benchmarks, we introduce a principled method for generating diverse questions. |
Zhen Jia; Philipp Christmann; Gerhard Weikum; |
| 357 | Optimal Engagement-Diversity Tradeoffs in Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is widely understood that this practice gives rise to echo chambers – users are mainly exposed to opinions that are similar to their own. In this paper, we ask whether echo chambers are an inevitable result of high engagement; we address this question in a novel model. |
Fabian Baumann; Daniel Halpern; Ariel D. Procaccia; Iyad Rahwan; Itai Shapira; Manuel W\{u}thrich; |
| 358 | GEES: Enabling Location Privacy-Preserving Energy Saving in Multi-Access Edge Computing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve the LEDR problem effectively and efficiently, we propose a system named GEES by incorporating differential geo-obfuscation to secure user privacy while maximizing system utility and energy efficiency through inferences with theoretical analysis. |
Ziqi Wang; Xiaoyu Xia; Minhui Xue; Ibrahim Khalil; Minghui Liwang; Xun Yi; |
| 359 | A Similarity-based Approach for Efficient Large Quasi-clique Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our key insight is that vertices within a quasi-clique exhibit similar neighborhoods to some extent. Based on this, we introduce NBSim and FastNBSim, efficient algorithms that find near-maximum quasi-cliques by exploiting vertex neighborhood similarity. |
Jiayang Pang; Chenhao Ma; Yixiang Fang; |
| 360 | Is It Safe to Share Your Files? An Empirical Security Analysis of Google Workspace Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we conduct a systematic study on the effectiveness of the cross-entity resource management in Google Workspace, the most popular BCP. |
Liuhuo Wan; Kailong Wang; Haoyu Wang; Guangdong Bai; |
| 361 | Don’t Bite Off More Than You Can Chew: Investigating Excessive Permission Requests in Trigger-Action Integrations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce PFCon, a system that leverages GPT-based language models for analyzing required and requested permissions, revealing excessive permission requests in a large-scale study of IFTTT TAP. |
Liuhuo Wan; Kailong Wang; Kulani Mahadewa; Haoyu Wang; Guangdong Bai; |
| 362 | Interpretable Knowledge Tracing with Multiscale State Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, achieving a high-performing model while ensuring interpretability presents a challenge. Therefore, in this paper, we propose a novel approach called Multiscale-state-based Interpretable Knowledge Tracing (MIKT). |
Jianwen Sun; Fenghua Yu; Qian Wan; Qing Li; Sannyuya Liu; Xiaoxuan Shen; |
| 363 | Making Cloud Spot Instance Interruption Events Visible Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To help users lower the chance of interruption of the spot instance for reliable usage, in this paper, we thoroughly analyze various datasets of the spot instance and present the feasibility for value prediction. |
KyungHwan Kim; Kyungyong Lee; |
| 364 | TATKC: A Temporal Graph Neural Network for Fast Approximate Temporal Katz Centrality Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The computation of traditional temporal Katz centrality is computationally expensive, especially when applied to massive temporal graphs. Therefore, in this paper, we design a temporal graph neural network to approximate temporal Katz centrality computation. |
Tianming Zhang; Junkai Fang; Zhengyi Yang; Bin Cao; Jing Fan; |
| 365 | Efficient Exact and Approximate Betweenness Centrality Computation for Temporal Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, in this paper, we target efficient methods for temporal betweenness centrality computation. |
Tianming Zhang; Yunjun Gao; Jie Zhao; Lu Chen; Lu Jin; Zhengyi Yang; Bin Cao; Jing Fan; |
| 366 | GNNShap: Scalable and Accurate GNN Explanation Using Shapley Values Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Some studies have proposed Shapley value based GNN explanations, yet they have several limitations: they consider limited samples to approximate Shapley values; some mainly focus on small and large coalition sizes, and they are an order of magnitude slower than other explanation methods, making them inapplicable to even moderate-size graphs. In this work, we propose GNNShap, which provides explanations for edges since they provide more natural explanations for graphs and more fine-grained explanations. |
Selahattin Akkas; Ariful Azad; |
| 367 | Medusa: Unveil Memory Exhaustion DoS Vulnerabilities in Protocol Implementations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This is because such vulnerabilities can deplete the system resources, leading to the unavailability of not only the vulnerable service but also other services running on the same machine. Despite the significance of this type of vulnerability, there has been limited research in this area.In this paper, we propose Medusa, a dynamic analysis framework to detect memory exhaustion vulnerabilities in protocol implementations, which are the most common type of resource exhaustion vulnerabilities. |
Zhengjie Du; Yuekang Li; Yaowen Zheng; Xiaohan Zhang; Cen Zhang; Yi Liu; Sheikh Mahbub Habib; Xinghua Li; Linzhang Wang; Yang Liu; Bing Mao; |
| 368 | Malicious Package Detection Using Metadata Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, due to the sheer volume of new packages being released daily, the task of identifying malicious packages presents a significant challenge. To address this issue, in this paper, we introduce a metadata-based malicious package detection model, MeMPtec. |
Sajal Halder; Michael Bewong; Arash Mahboubi; Yinhao Jiang; Md Rafiqul Islam; Md Zahid Islam; Ryan HL Ip; Muhammad Ejaz Ahmed; Gowri Sankar Ramachandran; Muhammad Ali Babar; |
| 369 | Using Model Calibration to Evaluate Link Prediction in Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an alternative protocol based on posterior probabilities of positives rather than ranks. |
Aishwarya Rao; Narayanan Asuri Krishnan; Carlos R. Rivero; |
| 370 | Causal Question Answering with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, in this paper, we aim to answer causal questions with a causality graph, a large-scale dataset of causal relations between noun phrases along with the relations’ provenance data. |
Lukas Bl\{u}baum; Stefan Heindorf; |
| 371 | FreqMAE: Frequency-Aware Masked Autoencoder for Multi-Modal IoT Sensing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents FreqMAE, a novel self-supervised learning framework that synergizes masked autoencoding (MAE) with physics-informed insights to capture feature patterns in multi-modal IoT sensor data. |
Denizhan Kara; Tomoyoshi Kimura; Shengzhong Liu; Jinyang Li; Dongxin Liu; Tianshi Wang; Ruijie Wang; Yizhuo Chen; Yigong Hu; Tarek Abdelzaher; |
| 372 | Markovletics: Methods and A Novel Application for Learning Continuous-Time Markov Chain Mixtures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The intrigue in CTMC mixtures stems from their ability to model intricate continuous-time stochastic processes prevalent in various fields including social media, finance, and biology.In this study, we introduce a novel framework for exploring CTMCs, emphasizing the influence of observed trails’ length and mixture parameters on problem regimes, which demands specific algorithms. |
Fabian Spaeh; Charalampos E. Tsourakakis; |
| 373 | How Contentious Terms About People and Cultures Are Used in Linked Open Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We study how frequently and in which literals contentious terms about people and cultures occur in LOD and whether there are attempts to mark the usage of such terms. |
Andrei Nesterov; Laura Hollink; Jacco van Ossenbruggen; |
| 374 | Revisiting The Behavioral Foundations of User Modeling Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: More generally, behavioral biases and inconsistent preferences make it highly challenging to appropriately interpret the user data that we observe. We discuss a set of models and algorithms that address this challenge through a process of inversion, in which an algorithm must try inferring mental states that are not directly measured in the data. |
Jon Kleinberg; |
| 375 | AI Deepfakes on The Web: The ‘Wicked’ Challenges for AI Ethics, Law and Technology Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This presentation addresses these problematic choices in responding to the ‘wicked’ challenge of AI deepfakes on the Web. It proposes a networked response to the problem, embracing multiple relevant actors and influences. |
Jeannie Marie Paterson; |
| 376 | Data Exchange Markets Via Utility Balancing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our market design approach for data sharing focuses on interim utility balance, where participants contribute and receive equitable utility from refinement of their models. We present such a market model for which we study computational complexity, solution existence, and approximation algorithms for welfare maximization and core stability. |
Aditya Bhaskara; Sreenivas Gollapudi; Sungjin Im; Kostas Kollias; Kamesh Munagala; Govind S. Sankar; |
| 377 | Identifying Risky Vendors in Cryptocurrency P2P Marketplaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work documents the online safety of cryptocurrency P2P marketplaces, identifies underlying issues in feedback-based reputation systems, and proposes improved mechanisms for predicting/monitoring risky accounts. |
Taro Tsuchiya; Alejandro Cuevas; Nicolas Christin; |
| 378 | Barter Exchange with Shared Item Valuations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider a centralized barter exchange with a set of agents and items where each item has a positive value. |
Juan Luque; Sharmila Duppala; John Dickerson; Aravind Srinivasan; |
| 379 | Hierarchical Position Embedding of Graphs with Landmarks and Clustering for Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a representation of positional information using representative nodes called landmarks. |
Minsang Kim; Seung Baek; |
| 380 | Descriptive Kernel Convolution Network with Improved Random Walk Kernel Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we first revisit the RWK and its current usage in KCNs, revealing several shortcomings of the existing designs, and propose an improved graph kernel RWK^+ , by introducing color-matching random walks and deriving its efficient computation. We then propose RWK^+ CN, a KCN that uses RWK^+ as the core kernel to learn descriptive graph features with an unsupervised objective, which can not be achieved by GNNs. |
Meng-Chieh Lee; Lingxiao Zhao; Leman Akoglu; |
| 381 | Extracting Small Subgraphs in Road Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this framework, it is critical for the subgraph to (a) be small and (b) include (near) optimal routes for a collection of customizations. This is precisely the setting that we study in this work. |
Sara Ahmadian; Sreenivas Gollapudi; Gregory Hutchins; Kostas Kollias; Xizhi Tan; |
| 382 | Fast and Accurate Fair K-Center Clustering in Doubling Metrics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a coreset-based approach to fair k-center clustering for general metric spaces which attains almost the best approximation quality of the current state of the art solutions, while featuring running times which can be orders of magnitude faster for large datasets of low doubling dimension. |
Matteo Ceccarello; Andrea Pietracaprina; Geppino Pucci; |
| 383 | A Quasi-Wasserstein Loss for Learning Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: When learning graph neural networks (GNNs) in node-level prediction tasks, most existing loss functions are applied for each node independently, even if node embeddings and their labels are non-i.i.d. because of their graph structures. To eliminate such inconsistency, in this study we propose a novel Quasi-Wasserstein (QW) loss with the help of the optimal transport defined on graphs, leading to new learning and prediction paradigms of GNNs. |
Minjie Cheng; Hongteng Xu; |
| 384 | Decoupled Variational Graph Autoencoder for Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a stochastic VGAE-based method that can effectively decouple the norm and angle in the embeddings. |
Yoon-Sik Cho; |
| 385 | Differentially Private Selection from Secure Distributed Computing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we design a protocol for differentially private selection in a trust setting similar to the shuffle model—with the crucial difference that our protocol tolerates corrupted servers while maintaining privacy. |
Ivan Damg\r{a}rd; Hannah Keller; Boel Nelson; Claudio Orlandi; Rasmus Pagh; |
| 386 | Uncovering The Hidden Data Costs of Mobile YouTube Video Ads Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we perform the first independent and empirical analysis of the data costs of mobile video ads on YouTube, the most popular video platform, from the users’ perspective. |
Emaan Atique; Saad Sher Alam; Harris Ahmad; Ihsan Ayyub Qazi; Zafar Ayyub Qazi; |
| 387 | Analyzing Ad Exposure and Content in Child-Oriented Videos on YouTube Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our analysis reveals that the safety of a child’s YouTube experience is shaped significantly by their external environment. |
Emaan Bilal Khan; Nida Tanveer; Aima Shahid; Mohammad Jaffer Iqbal; Haashim Ali Mirza; Armish Javed; Ihsan Ayyub Qazi; Zafar Ayyub Qazi; |
| 388 | Automating Website Registration for Studying GDPR Compliance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, research based on automated measurements has been limited to those websites that do not require user authentication. To overcome this limitation, we developed a crawler that automates website registrations and newsletter subscriptions and detects both security and privacy threats at scale.We demonstrate our crawler’s capabilities by running it on 660k websites. |
Karel Kubicek; Jakob Merane; Ahmed Bouhoula; David Basin; |
| 389 | A Fast Similarity Matrix Calibration Method with Incomplete Query Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, incomplete observations are ubiquitous in real scenarios leading to a less accurate similarity matrix. To alleviate this problem, in this paper, based on the key insight that the similarity matrix enjoys both the symmetric and positive semi-definiteness (PSD) properties, we propose a novel similarity matrix calibration method, which is scalable, effective, and sound. |
Changyi Ma; Runsheng Yu; Youzhi Zhang; |
| 390 | Benchmark and Neural Architecture for Conversational Entity Retrieval from A Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel information retrieval (IR) task of Conversational Entity Retrieval from a Knowledge Graph (CER-KG), which extends non-conversational entity retrieval from a knowledge graph (KG) to the conversational scenario. |
Mona Zamiri; Yao Qiang; Fedor Nikolaev; Dongxiao Zhu; Alexander Kotov; |
| 391 | GRASP: Hardening Serverless Applications Through Graph Reachability Analysis of Security Policies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, with role-based access control solutions like Amazon Identity and Access Management (IAM) already suffering from pervasive misconfiguration problems, the likelihood of policy failures in serverless applications is high.In this work, we introduce GRASP, a graph-based analysis framework for modeling serverless access control policies as queryable reachability graphs. |
Isaac Polinsky; Pubali Datta; Adam Bates; William Enck; |
| 392 | Unfiltered: Measuring Cloud-based Email Filtering Bypasses Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this loosely coupled approach can, in theory, be bypassed if the email hosting provider is not configured to only accept messages that arrive from the email filtering service. In this paper we demonstrate that such bypasses are commonly possible. |
Sumanth Rao; Enze Liu; Grant Ho; Geoffrey M. Voelker; Stefan Savage; |
| 393 | Phishing Vs. Legit: Comparative Analysis of Client-Side Resources of Phishing and Target Brand Websites Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we gain a deeper understanding of how client-side resources (especially, JavaScript libraries) are used in phishing websites by comparing them with the resources used in the legitimate target websites. |
Kyungchan Lim; Jaehwan Park; Doowon Kim; |
| 394 | The Matter of Captchas: An Analysis of A Brittle Security Feature on The Modern Web Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper focuses on a fragile but still in-use security feature, text-based CAPTCHAs, that had been wildly used by web applications in the past to protect against automated attacks such as credential stuffing and account hijacking. |
Behzad Ousat; Esteban Schafir; Duc C. Hoang; Mohammad Ali Tofighi; Cuong V. Nguyen; Sajjad Arshad; Selcuk Uluagac; Amin Kharraz; |
| 395 | Discovering and Measuring CDNs Prone to Domain Fronting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we leverage passive and active DNS traffic analysis to pinpoint domain names served by CDNs and build an automated tool that can be used to discover CDNs that allow domain fronting in their infrastructure. |
Karthika Subramani; Roberto Perdisci; Pierros-Christos Skafidas; Manos Antonakakis; |
| 396 | PanoptiChrome: A Modern In-browser Taint Analysis Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We outline the details of a multi-year effort in this paper that led to PanoptiChrome, which accurately tracks information flow across an arbitrary number of sources and sinks and is, to a large extent, portable across platforms. |
Rahul Kanyal; Smruti R. Sarangi; |
| 397 | Fingerprinting The Shadows: Unmasking Malicious Servers with Machine Learning-Powered TLS Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we examine state-of-the-art fingerprinting techniques and extend a machine learning pipeline for effective and practical server classification. |
Andreas Theofanous; Eva Papadogiannaki; Alexander Shevtsov; Sotiris Ioannidis; |
| 398 | Efficient Computation of Signature-Restricted Views for Semantic Web Ontologies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In some instances, it is not even feasible to compute a uniform interpolant; when feasible, the size of the uniform interpolant can be up to triple exponentially larger than the source ontology. Despite these challenges, our paper introduces an improved ”forgetting” technique specifically designed for computing uniform interpolants of ELI-ontologies. |
Yizheng Zhao; |
| 399 | A Method for Assessing Inference Patterns Captured By Embedding Models in Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a model-agnostic method to empirically quantify how patterns are captured by trained embedding models. |
Narayanan Asuri Krishnan; Carlos R. Rivero; |
| 400 | Follow The Path: Hierarchy-Aware Extreme Multi-Label Completion for Semantic Text Tagging Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most XML completion problems can organically leverage a label hierarchy, which can be represented as a tree that encodes the relations between the different labels.In this paper, we propose a new algorithm, HECTOR – Hierarchical Extreme Completion for Text based on TransfORmer, to solve XML Completion problems more effectively. |
Natalia Ostapuk; Julien Audiffren; Ljiljana Dolamic; Alain Mermoud; Philippe Cudr\'{e}-Mauroux; |
| 401 | Query Optimization for Ontology-Mediated Query Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We devise a novel optimization framework for a large set of OMQA settings that enjoy FO-rewriting: conjunctive queries, i.e., the core select-project-join queries, asked on KBs expressed using datalog+/-, description logics, existential rules, OWL, or RDFS. |
Wafaa El Husseini; Cheikh Brahim El Vaigh; Fran\c{c}ois Goasdou\'{e}; H\'{e}l\`{e}ne Jaudoin; |
| 402 | A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel inductive relation prediction model named SymRITa with a logic transformer integrating rules. |
Yudai Pan; Jun Liu; Tianzhe Zhao; Lingling Zhang; Yun Lin; Jin Song Dong; |
| 403 | Multi-Label Zero-Shot Product Attribute-Value Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel method to efficiently and effectively extract unseen attribute values from new products in the absence of labeled data (zero-shot setting). |
Jiaying Gong; Hoda Eldardiry; |
| 404 | Aligning Out-of-Distribution Web Images and Caption Semantics Via Evidential Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on empirical observations, we introduce a novel approach, Evidential Language-Image Posterior (ELIP), to achieve robust alignment between web images and semantic knowledge across various OOD cases by leveraging evidential uncertainties. |
Guohao Sun; Yue Bai; Xueying Yang; Yi Fang; Yun Fu; Zhiqiang Tao; |
| 405 | FedUP: Querying Large-Scale Federations of SPARQL Endpoints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the concept of Result-Aware query plans. |
Julien Aimonier-Davat; Brice N\'{e}delec; Minh-Hoang Dang; Pascal Molli; Hala Skaf-Molli; |
| 406 | Deliberate Exposure to Opposing Views and Its Association with Behavior and Rewards on Political Communities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Engaging with diverse political views is important for reaching better collective decisions, however, users online tend to remain confined within ideologically homogeneous spaces. In this work, we study users who are members of these spaces but who also show a willingness to engage with diverse views, as they have the potential to introduce more informational diversity into their communities. |
Alexandros Efstratiou; |
| 407 | Team Formation Amidst Conflicts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we formulate the problem of team formation amidst conflicts. |
Iasonas Nikolaou; Evimaria Terzi; |
| 408 | Labor Space: A Unifying Representation of The Labor Market Via Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we introduce Labor Space, a vector-space embedding of heterogeneous labor market entities, derived through applying a large language model with fine-tuning. |
Seongwoon Kim; Yong-Yeol Ahn; Jaehyuk Park; |
| 409 | Unraveling The Dynamics of Stable and Curious Audiences in Web Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the Burst-Induced Poisson Process (BPoP), a model designed to analyze time series data such as feeds or search queries. |
Rodrigo Alves; Antoine Ledent; Renato Assun\c{c}\~{a}o; Pedro Vaz-De-Melo; Marius Kloft; |
| 410 | NETEVOLVE: Social Network Forecasting Using Multi-Agent Reinforcement Learning with Interpretable Features Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose NetEvolve a novel multi-agent reinforcement learning-based method that predicts changes in a given social network. |
Kentaro Miyake; Hiroyoshi Ito; Christos Faloutsos; Hirotomo Matsumoto; Atsuyuki Morishima; |
| 411 | Analysis and Detection of Pink Slime Websites in Social Media Posts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By analyzing a large dataset of posts, we gain valuable insights into the patterns of these posts and the origin of these posts. We show in this work that extracting syntactical features proves valuable in developing a classification approach for detecting such posts and that the approach achieves 92.5\% accuracy. |
Abdullah Aljebreen; Weiyi Meng; Eduard C. Dragut; |
| 412 | Fairness Rising from The Ranks: HITS and PageRank on Homophilic Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the conditions under which link analysis algorithms prevent minority groups from reaching high ranking slots. |
Ana-Andreea Stoica; Nelly Litvak; Augustin Chaintreau; |
| 413 | Bots, Elections, and Controversies: Twitter Insights from Brazil’s Polarised Elections Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work can be instrumental in dismantling coordinated campaigns and offer valuable insights for the enhancement of bot detection algorithms. |
Diogo Pacheco; |
| 414 | Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a new measure for an individual’s group conformity based on contextualized embeddings of tweet text, which helps us assess polarization between religious groups. |
Rochana Chaturvedi; Sugat Chaturvedi; Elena Zheleva; |
| 415 | PASS: Predictive Auto-Scaling System for Large-scale Enterprise Web Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We confront two challenges in the management of a vast and diverse array of online web applications deployed on enterprise-grade auto-scaling infrastructure, primarily focused on ensuring Quality of Service (QoS) for large-scale applications and optimizing resource costs. |
Yunda Guo; Jiake Ge; Panfeng Guo; Yunpeng Chai; Tao Li; Mengnan Shi; Yang Tu; Jian Ouyang; |
| 416 | Investigations of Top-Level Domain Name Collisions in Blockchain Naming Services Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we investigated BNS TLD name collisions by analyzing TLDs registered on two BNSs: Handshake and Decentraweb. |
Daiki Ito; Yuta Takata; Hiroshi Kumagai; Masaki Kamizono; |
| 417 | Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, our study reveals that VAE-based methods face challenges in capturing long-periodic heterogeneous patterns and detailed short-periodic trends simultaneously. To address these challenges, we propose Frequency-enhanced Conditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for univariate time series. |
Zexin Wang; Changhua Pei; Minghua Ma; Xin Wang; Zhihan Li; Dan Pei; Saravan Rajmohan; Dongmei Zhang; Qingwei Lin; Haiming Zhang; Jianhui Li; Gaogang Xie; |
| 418 | Cold-start Bundle Recommendation Via Popularity-based Coalescence and Curriculum Heating Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose CoHeat (Popularity-based Coalescence and Curriculum Heating), an accurate approach for cold-start bundle recommendation. |
Hyunsik Jeon; Jong-eun Lee; Jeongin Yun; U Kang; |
| 419 | UnifiedSSR: A Unified Framework of Sequential Search and Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a Unified framework of Sequential Search and Recommendation (UnifiedSSR) for joint learning of user behavior history in both search and recommendation scenarios. |
Jiayi Xie; Shang Liu; Gao Cong; Zhenzhong Chen; |
| 420 | Modeling Balanced Explicit and Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods fail to consider these implicit relations, and these relations themselves are difficult to learn and represent, causing poor performance in knowledge concept recommendation and an inability to meet users’ personalized needs. To address this issue, we propose a novel framework based on contrastive learning, which can represent and balance the explicit and implicit relations for knowledge concept recommendation in MOOCs (CL-KCRec). |
Hengnian Gu; Zhiyi Duan; Pan Xie; Dongdai Zhou; |
| 421 | TikTok and The Art of Personalization: Investigating Exploration and Exploitation on Social Media Feeds Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a general framework to examine a set of social media feed recommendations for a user as a timeline. |
Karan Vombatkere; Sepehr Mousavi; Savvas Zannettou; Franziska Roesner; Krishna P. Gummadi; |
| 422 | Predictive Relevance Uncertainty for Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we analyse popular uncertainty methods in the context of recommendation system. |
Charul Paliwal; Anirban Majumder; Sivaramakrishnan Kaveri; |
| 423 | Dynamic Multi-Network Mining of Tensor Time Series Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new method, Dynamic Multi-network Mining (DMM), that converts a tensor time series into a set of segment groups of various lengths (i.e., clusters) characterized by a dependency network constrained with l1-norm. |
Kohei Obata; Koki Kawabata; Yasuko Matsubara; Yasushi Sakurai; |
| 424 | Diagrammatic Reasoning for ALC Visualization with Logic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The purpose of this research is to facilitate the usage of OWL ontologies by providing a diagrammatic reasoning system over their visual representations. |
Ildar Baimuratov; |
| 425 | Distributed Data Placement and Content Delivery in Web Caches with Non-Metric Access Costs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by applications in web caches and content delivery in peer-to-peer networks, we consider the non-metric data placement problem and develop distributed algorithms for computing or approximating its optimal solutions. |
S. Rasoul Etesami; |
| 426 | MMLSCU: A Dataset for Multi-modal Multi-domain Live Streaming Comment Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce the MMLSCU dataset, containing 50,129 intention-annotated comments across multiple modalities (text, images, vi-deos, audio) from eight streaming domains. |
Zixiang Meng; Qiang Gao; Di Guo; Yunlong Li; Bobo Li; Hao Fei; Shengqiong Wu; Fei Li; Chong Teng; Donghong Ji; |
| 427 | DenseFlow: Spotting Cryptocurrency Money Laundering in Ethereum Transaction Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, the existence of dense and extensive laundering gangs and intricate multilayered laundering pathways makes it exceptionally challenging for regulators to identify suspicious accounts and trace money flows. To address this issue, we propose an innovative DenseFlow framework that effectively identifies and traces money laundering activities by finding dense subgraphs and applying the maximum flow idea. |
Dan Lin; Jiajing Wu; Yunmei Yu; Qishuang Fu; Zibin Zheng; Changlin Yang; |
| 428 | POLISH: Adaptive Online Cross-Modal Hashing for Class Incremental Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This assumption does not hold in real-world scenarios, and the presence of new labels in incoming data chunks can severely degrade or even break these methods. To tackle these issues, we introduce a novel supervised online cross-modal hashing method named adaPtive Online cLass-Incremental haSHing (POLISH). |
Yu-Wei Zhan; Xin Luo; Zhen-Duo Chen; Yongxin Wang; Yinwei Wei; Xin-Shun Xu; |
| 429 | Human Vs ChatGPT: Effect of Data Annotation in Interpretable Crisis-Related Microblog Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the recent advances in large language models (LLMs) as data annotators on informal tweet text. |
Thi Huyen Nguyen; Koustav Rudra; |
| 430 | Triage of Messages and Conversations in A Large-Scale Child Victimization Corpus Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop both supervised and unsupervised methods to classify messages into categories of interest to law enforcement, such as age requests, persuasion, and sexual messages. |
Prasanna Lakkur Subramanyam; Mohit Iyyer; Brian N. Levine; |
| 431 | Unveiling Climate Drivers Via Feature Importance Shift Analysis in New Zealand Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose DUO, a framework to identify shifts in important features and feature combinations as the data distribution changes over time. |
Bowen Chen; Gillian Dobbie; Neelesh Rampal; Yun Sing Koh; |
| 432 | Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Response Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop an infrastructure ombudsman — that automatically detects specific infrastructure concerns. |
Md Towhidul Absar Chowdhury; Soumyajit Datta; Naveen Sharma; Ashiqur R. KhudaBukhsh; |