Paper Digest: WWW 2023 Highlights
To search or review papers within WWW-2023 related to a specific topic, please use the search by venue and review by venue services. To browse papers by author, here is a list of top authors (WWW-2023). You may also like to explore our “Best Paper” Digest (WWW), which lists the most influential WWW papers since 2001.
The Web Conference (WWW) is one of the top internet conferences in the world. In 2023, it is to be held in Austin, Texas.
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TABLE 1: Paper Digest: WWW 2023 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Using Diversity As A Source of Scientific Innovation for The Web Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this talk, I’ll discuss how geographical, language, and social diversity have opened new avenues for innovation and better understanding the social Web. |
Barbara Poblete; |
| 2 | Decolonizing Creative Labor in The Age of AI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This talk discusses concerns around digital labor, data materiality, media literacies, creative value, and online expression. |
Payal Arora; |
| 3 | Concept Regulation in The Social Sciences Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The sciences, notably biology and medicine, operate with highly regulated taxonomies and ontologies. The Social Sciences, on the other hand, muddle through in a proverbial tower … |
Zachary Elkins; |
| 4 | GNNs and Graph Generative Models for Biomedical Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this talk we will present the potential of graph generative models and our recent relevant efforts in the biomedical domain. |
Michalis Vazirgiannis; |
| 5 | CONNECTIVITY Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The most important new fact about the human condition is that we are now suddenly connected. When I say “suddenly” I refer to the Internet’s birthday, October 29, 1969 and how two … |
Robert Melancton Metcalfe; |
| 6 | GELTOR: A Graph Embedding Method Based on Listwise Learning to Rank Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first point out the three drawbacks of existing similarity-based embedding methods: inaccurate similarity computation, conflicting optimization goal, and impairing in/out-degree distributions. Then, motivated by these drawbacks, we propose AdaSim*, a novel similarity measure for graphs that is conducive to the similarity-based graph embedding. We finally propose GELTOR, an effective embedding method that employs AdaSim* as a node similarity measure and the concept of learning-to-rank in the embedding process. |
Masoud Reyhani Hamedani; Jin-Su Ryu; Sang-Wook Kim; |
| 7 | Graph-less Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In addition, the recursive information propagation with the stacked aggregators in the entire graph structures may result in poor scalability in practical applications. Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning. |
Lianghao Xia; Chao Huang; Jiao Shi; Yong Xu; |
| 8 | Fair Graph Representation Learning Via Diverse Mixture-of-Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although many works were proposed to address the fairness issue, they suffer from the significant problem of insufficient learnable knowledge with limited attributes after debiasing. To address this problem, we develop Graph-Fairness Mixture of Experts (G-Fame), a novel plug-and-play method to assist any GNNs to learn distinguishable representations with unbiased attributes. |
Zheyuan Liu; Chunhui Zhang; Yijun Tian; Erchi Zhang; Chao Huang; Yanfang Ye; Chuxu Zhang; |
| 9 | Multi-Aspect Heterogeneous Graph Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Multi-Aspect Heterogeneous Graph Augmentation framework named MAHGA. |
Yuchen Zhou; Yanan Cao; Yongchao Liu; Yanmin Shang; Peng Zhang; Zheng Lin; Yun Yue; Baokun Wang; Xing Fu; Weiqiang Wang; |
| 10 | Testing Cluster Properties of Signed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our main technical contribution is a sublinear algorithm for testing clusterability in the bounded-degree model. |
Florian Adriaens; Simon Apers; |
| 11 | RSGNN: A Model-agnostic Approach for Enhancing The Robustness of Signed Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Signed graphs model complex relations using both positive and negative edges. Signed graph neural networks (SGNN) are powerful tools to analyze signed graphs. We address the vulnerability of SGNN to potential edge noise in the input graph. |
Zeyu Zhang; Jiamou Liu; Xianda Zheng; Yifei Wang; Pengqian Han; Yupan Wang; Kaiqi Zhao; Zijian Zhang; |
| 12 | NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose NeuKron for compressing a sparse reorderable matrix into a constant-size space. |
Taehyung Kwon; Jihoon Ko; Jinhong Jung; Kijung Shin; |
| 13 | Multi-aspect Diffusion Network Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study how to infer a multi-aspect diffusion network with heterogeneous influence relationships, using only node infection statuses that are more readily accessible in practice. |
Hao Huang; Keqi Han; Beicheng Xu; Ting Gan; |
| 14 | Collaboration-Aware Graph Convolutional Network for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: After demonstrating the benefits of leveraging collaborations from neighbors with higher CIR, we propose a recommendation-tailored GNN, Collaboration-Aware Graph Convolutional Network (CAGCN), that goes beyond 1-Weisfeiler-Lehman(1-WL) test in distinguishing non-bipartite-subgraph-isomorphic graphs. |
Yu Wang; Yuying Zhao; Yi Zhang; Tyler Derr; |
| 15 | Pairwise-interactions-based Bayesian Inference of Network Structure from Information Cascades Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although many methods for inferring a network structure from observed cascades have been proposed, they did not perceive the relationship between pairwise interactions in a cascade. Therefore, this paper proposes a Pairwise-interactions-based Bayesian Inference method (named PBI) to infer the underlying diffusion network structure. |
Chao Gao; Yuchen Wang; Zhen Wang; Xianghua Li; Xuelong Li; |
| 16 | Encoding Node Diffusion Competence and Role Significance for Network Dismantling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an unsupervised learning framework for network dismantling, called DCRS, which encodes and fuses both node diffusion competence and role significance. |
Jiazheng Zhang; Bang Wang; |
| 17 | Hierarchical Knowledge Graph Learning Enabled Socioeconomic Indicator Prediction in Location-Based Social Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we first construct a location-based KG (LBKG) to integrate various kinds of knowledge from heterogeneous LBSN data, including locations and other related elements like point of interests (POIs), business areas as well as various relationships between them, such as spatial proximity and functional similarity. Then we propose a hierarchical KG learning model to capture both global knowledge from LBKG and domain knowledge from several sub-KGs. |
Zhilun Zhou; Yu Liu; Jingtao Ding; Depeng Jin; Yong Li; |
| 18 | Opinion Maximization in Social Networks Via Leader Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study a leader selection problem for the DeGroot model of opinion dynamics in a social network with n nodes and m edges, in the presence of s0 = O(1) leaders with opinion 0. |
Xiaotian Zhou; Zhongzhi Zhang; |
| 19 | SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to comprehensively enhance the performance of generative graph SSL against other GCL models on both unsupervised and supervised learning tasks, we propose the SeeGera model, which is based on the family of self-supervised variational graph auto-encoder (VGAE). |
Xiang Li; Tiandi Ye; Caihua Shan; Dongsheng Li; Ming Gao; |
| 20 | Graph Self-supervised Learning with Augmentation-aware Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Graph Self-supervised Learning method with Augmentation-aware Contrastive Learning. |
Dong Chen; Xiang Zhao; Wei Wang; Zhen Tan; Weidong Xiao; |
| 21 | Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenges, we propose a Hierarchy-Aware Dual Clustering Graph Network (HADCG) model for session-based recommendation. |
Jiajie Su; Chaochao Chen; Weiming Liu; Fei Wu; Xiaolin Zheng; Haoming Lyu; |
| 22 | Unifying and Improving Graph Convolutional Neural Networks with Wavelet Denoising Filters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the theories from graph signal processing (GSP), we show that GCN’s capability is fundamentally limited by the uncertainty principle, and wavelets provide a controllable trade-off between local and global information. |
Liangtian Wan; Xiaona Li; Huijin Han; Xiaoran Yan; Lu Sun; Zhaolong Ning; Feng Xia; |
| 23 | HyConvE: A Novel Embedding Model for Knowledge Hypergraph Link Prediction with Convolutional Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel embedding-based knowledge hypergraph link prediction model named HyConvE, which exploits the powerful learning ability of convolutional neural networks for effective link prediction. |
Chenxu Wang; Xin Wang; Zhao Li; Zirui Chen; Jianxin Li; |
| 24 | Efficient Approximation Algorithms for The Diameter-Bounded Max-Coverage Group Steiner Tree Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast to its specialization—the classic Group Steiner Tree (GST) problem which has been extensively studied, the emerging DCGST problem still lacks an efficient algorithm. In this paper, we propose Cba, the first approximation algorithm for the DCGST problem, and we prove its worst-case approximation ratio. |
Ke Zhang; Xiaoqing Wang; Gong Cheng; |
| 25 | Neighborhood Structure Configuration Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a new method to efficiently sample synthetic networks that preserve the d-hop neighborhood structure of a given network for any given d. |
Felix I. Stamm; Michael Scholkemper; Markus Strohmaier; Michael T. Schaub; |
| 26 | CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a new Curvature-based topology-aware Dropout sampling technique named CurvDrop, in which we integrate the Discrete Ricci Curvature into graph neural networks to enable more expressive graph models. |
Yang Liu; Chuan Zhou; Shirui Pan; Jia Wu; Zhao Li; Hongyang Chen; Peng Zhang; |
| 27 | Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While a number of directed network embedding methods have been proposed, we empirically show that the existing methods lack out-of-distribution generalization abilities against degree-related distributional shifts. To mitigate this problem, we propose ODIN (Out-of-Distribution Generalized Directed Network Embedding), a new directed NE method where we model multiple factors in the formation of directed edges. |
Hyunsik Yoo; Yeon-Chang Lee; Kijung Shin; Sang-Wook Kim; |
| 28 | ConsRec: Learning Consensus Behind Interactions for Group Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To solve the aforementioned limitations, in this paper, we focus on exploring consensus behind group behavior data. |
Xixi Wu; Yun Xiong; Yao Zhang; Yizhu Jiao; Jiawei Zhang; Yangyong Zhu; Philip S. Yu; |
| 29 | A Post-Training Framework for Improving Heterogeneous Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A possible explanation is that their message passing mechanisms may involve noises from different categories, and cannot fully explore task-specific knowledge such as the label dependency between distant nodes. Therefore, instead of introducing a new HGNN model, we propose a general post-training framework that can be applied on any pretrained HGNNs to further inject task-specific knowledge and enhance their prediction performance. |
Cheng Yang; Xumeng Gong; Chuan Shi; Philip Yu; |
| 30 | Link Prediction on Latent Heterogeneous Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the challenging and unexplored problem of link prediction on an LHG. |
Trung-Kien Nguyen; Zemin Liu; Yuan Fang; |
| 31 | Predicting The Silent Majority on Graphs: Knowledge Transferable Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, most existing Graph Neural Networks (GNNs) assume that all nodes belong to the same domain, without considering the missing features and distribution-shift between domains, leading to poor ability to deal with VS-Graph. To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KTGNN), which models distribution-shifts during message passing and learns representation by transferring knowledge from vocal nodes to silent nodes. |
Wendong Bi; Bingbing Xu; Xiaoqian Sun; Li Xu; Huawei Shen; Xueqi Cheng; |
| 32 | Lightweight Source Localization for Large-scale Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new localization strategy based on finite deployed sensors, named Greedy-coverage-based Rapid Source Localization (GRSL), to rapidly, flexibly and accurately infer the source in the early propagation stage of large-scale networks. |
Zhen Wang; Dongpeng Hou; Chao Gao; Xiaoyu Li; Xuelong Li; |
| 33 | Automated Spatio-Temporal Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In such cases, current methods are vulnerable to the quality of the generated region graphs, which may lead to suboptimal performance. In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources. |
Qianru Zhang; Chao Huang; Lianghao Xia; Zheng Wang; Zhonghang Li; Siuming Yiu; |
| 34 | Graph Neural Networks with Diverse Spectral Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the success, existing spectral GNNs usually fail to deal with complex networks (e.g., WWW) due to such homogeneous spectral filtering setting that ignores the regional heterogeneity as typically seen in real-world networks. To tackle this issue, we propose a novel diverse spectral filtering (DSF) framework, which automatically learns node-specific filter weights to exploit the varying local structure properly. |
Jingwei Guo; Kaizhu Huang; Xinping Yi; Rui Zhang; |
| 35 | Characterization of Simplicial Complexes By Counting Simplets Beyond Four Nodes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we concentrate on the local patterns in simplicial complexes called simplets, a generalization of graphlets. |
Hyunju Kim; Jihoon Ko; Fanchen Bu; Kijung Shin; |
| 36 | Robust Mid-Pass Filtering Graph Convolutional Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Extra training overhead is also required in their design. To address these challenges, we conduct in-depth explorations on mid-frequency signals on graphs and propose a simple yet effective Mid-pass filter GCN (Mid-GCN). |
Jincheng Huang; Lun Du; Xu Chen; Qiang Fu; Shi Han; Dongmei Zhang; |
| 37 | Semi-decentralized Federated Ego Graph Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, existing federated recommendation systems require resource-limited devices to maintain the entire embedding tables resulting in high communication costs. In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner. |
Liang Qu; Ningzhi Tang; Ruiqi Zheng; Quoc Viet Hung Nguyen; Zi Huang; Yuhui Shi; Hongzhi Yin; |
| 38 | XGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. |
Xiran Song; Jianxun Lian; Hong Huang; Zihan Luo; Wei Zhou; Xue Lin; Mingqi Wu; Chaozhuo Li; Xing Xie; Hai Jin; |
| 39 | SINCERE: Sequential Interaction Networks Representation Learning on Co-Evolving RiEmannian Manifolds Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead of residing in a single fixed curvature space, how will the representation spaces evolve when new interaction occurs? To explore these implication for sequential interaction networks, we propose SINCERE, a novel method representing Sequential Interaction Networks on Co-Evolving RiEmannian manifolds. |
Junda Ye; Zhongbao Zhang; Li Sun; Yang Yan; Feiyang Wang; Fuxin Ren; |
| 40 | PARROT: Position-Aware Regularized Optimal Transport for Network Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a position-aware regularized optimal transport framework for network alignment named PARROT. |
Zhichen Zeng; Si Zhang; Yinglong Xia; Hanghang Tong; |
| 41 | Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the cross-domain sequential recommendation problem. |
Weiming Liu; Xiaolin Zheng; Chaochao Chen; Jiajie Su; Xinting Liao; Mengling Hu; Yanchao Tan; |
| 42 | Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Considering that the neighbors’ features and the social relationships are very informative to characterize a user’s uplift, we propose a graph neural network-based framework with two uplift estimators, called GNUM, to learn from the social graph for uplift estimation. |
Dingyuan Zhu; Daixin Wang; Zhiqiang Zhang; Kun Kuang; Yan Zhang; Yulin Kang; Jun Zhou; |
| 43 | Label Information Enhanced Fraud Detection Against Low Homophily in Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: But we find they are less effective in fraud detection tasks due to the low homophily in graphs. In this work, we propose GAGA, a novel Group AGgregation enhanced TrAnsformer, to tackle the above challenges. |
Yuchen Wang; Jinghui Zhang; Zhengjie Huang; Weibin Li; Shikun Feng; Ziheng Ma; Yu Sun; Dianhai Yu; Fang Dong; Jiahui Jin; Beilun Wang; Junzhou Luo; |
| 44 | GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. |
Zemin Liu; Xingtong Yu; Yuan Fang; Xinming Zhang; |
| 45 | An Attentional Multi-scale Co-evolving Model for Dynamic Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes to model the inherent correlations among the evolving dynamics of different structural scales for dynamic link prediction. |
Guozhen Zhang; Tian Ye; Depeng Jin; Yong Li; |
| 46 | Robust Graph Representation Learning for Local Corruption Recovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work establishes a graph learning scheme that automatically detects (locally) corrupted feature attributes and recovers robust embedding for prediction tasks. |
Bingxin Zhou; Yuanhong Jiang; Yuguang Wang; Jingwei Liang; Junbin Gao; Shirui Pan; Xiaoqun Zhang; |
| 47 | Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Though researchers have proposed various CDR methods to effectively transfer knowledge across domains, they fail to address the following key issues, i.e., (1) they cannot model high-order correlations among users and items in every single domain to obtain more accurate representations; (2) they cannot model the correlations among items across different domains. To tackle the above issues, we propose a novel Intra and Inter Domain HyperGraph Convolutional Network (II-HGCN) framework, which includes two main layers in the modeling process, i.e., the intra-domain layer and the inter-domain layer. |
Zhongxuan Han; Xiaolin Zheng; Chaochao Chen; Wenjie Cheng; Yang Yao; |
| 48 | Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: It is well known that hyperbolic geometry has a unique advantage in representing the hierarchical structure of graphs. Therefore, we attempt to explore the hierarchy-imbalance issue for node classification of graph neural networks with a novelty perspective of hyperbolic geometry, including its characteristics and causes. |
Xingcheng Fu; Yuecen Wei; Qingyun Sun; Haonan Yuan; Jia Wu; Hao Peng; Jianxin Li; |
| 49 | Graph Neural Networks Without Propagation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Quantitative experimental analysis reveals: 1) the existence of low-rank characteristic in the node attributes from ego-networks and 2) the performance improvement by reducing its rank. Motivated by this finding, this paper propose the Low-Rank GNNs, whose key component is the low-rank attribute matrix approximation in ego-network. |
Liang Yang; Qiuliang Zhang; Runjie Shi; Wenmiao Zhou; Bingxin Niu; Chuan Wang; Xiaochun Cao; Dongxiao He; Zhen Wang; Yuanfang Guo; |
| 50 | TIGER: Temporal Interaction Graph Embedding with Restarts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This prevents existing models from parallelization and reduces their flexibility in industrial applications. To tackle the above challenge, in this paper, we propose TIGER, a TIG embedding model that can restart at any timestamp. |
Yao Zhang; Yun Xiong; Yongxiang Liao; Yiheng Sun; Yucheng Jin; Xuehao Zheng; Yangyong Zhu; |
| 51 | Self-Supervised Teaching and Learning of Representations on Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. |
Liangtian Wan; Zhenqiang Fu; Lu Sun; Xianpeng Wang; Gang Xu; Xiaoran Yan; Feng Xia; |
| 52 | SE-GSL: A General and Effective Graph Structure Learning Framework Through Structural Entropy Optimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a general GSL framework, SE-GSL, through structural entropy and the graph hierarchy abstracted in the encoding tree. |
Dongcheng Zou; Hao Peng; Xiang Huang; Renyu Yang; Jianxin Li; Jia Wu; Chunyang Liu; Philip S. Yu; |
| 53 | Homophily-oriented Heterogeneous Graph Rewiring Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, based on empirical observations, we propose a meta-path-induced metric to measure the homophily degree of a HG. |
Jiayan Guo; Lun Du; Wendong Bi; Qiang Fu; Xiaojun Ma; Xu Chen; Shi Han; Dongmei Zhang; Yan Zhang; |
| 54 | HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose HGWaveNet, a novel hyperbolic graph neural network that fully exploits the fitness between hyperbolic spaces and data distributions for temporal link prediction. |
Qijie Bai; Changli Nie; Haiwei Zhang; Dongming Zhao; Xiaojie Yuan; |
| 55 | Rethinking Structural Encodings: Adaptive Graph Transformer for Node Classification Task Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the node classification task, existing Graph Transformers with Positional Encodings are limited by the following issues: (i) PEs describing the node�s positional identities are insufficient for the node classification task on complex graphs, where a full portrayal of the local node property is needed. (ii) PEs for graphs are integrated with Transformers in a constant schema, resulting in the ignorance of local patterns that may vary among different nodes. In this paper, we propose Adaptive Graph Transformer (AGT) to tackle above issues. |
Xiaojun Ma; Qin Chen; Yi Wu; Guojie Song; Liang Wang; Bo Zheng; |
| 56 | CMINet: A Graph Learning Framework for Content-aware Multi-channel Influence Diffusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new influence estimation problem, namely Content-aware Multi-channel Influence Diffusion (CMID), and accordingly propose CMINet to predict newly influenced users, given a set of seed users with different multimedia contents. |
Hsi-Wen Chen; De-Nian Yang; Wang-Chien Lee; Philip S. Yu; Ming-Syan Chen; |
| 57 | Federated Node Classification Over Graphs with Latent Link-type Heterogeneity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose a framework FedLit that can dynamically detect the latent link-types during FL via an EM-based clustering algorithm and differentiate the message-passing through different types of links via multiple convolution channels. |
Han Xie; Li Xiong; Carl Yang; |
| 58 | Expressive and Efficient Representation Learning for Ranking Links in Temporal Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However it is not straightforward to develop a T-GRL method to optimize a ranking loss due to a tradeoff between model expressivity and scalability. In this work, we address these issues and propose a Temporal Graph network for Ranking (TGRank), which significantly improves performance for link prediction tasks by (i) optimizing a list-wise loss for improved ranking, and (ii) incorporating a labeling approach designed to allow for efficient inference over the candidate set jointly, while provably boosting expressivity. |
Susheel Suresh; Mayank Shrivastava; Arko Mukherjee; Jennifer Neville; Pan Li; |
| 59 | Semi-Supervised Embedding of Attributed Multiplex Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Semi-supervised Embedding approach for Attributed Multiplex Networks (SSAMN), to jointly embed nodes, node attributes, and node labels of multiplex networks in a low dimensional space. |
Ylli Sadikaj; Justus Rass; Yllka Velaj; Claudia Plant; |
| 60 | Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By transforming the two design needs into designing data-specific inter-layer connections, we propose a novel approach with the help of neural architecture search (NAS), which is dubbed LRGNN (Long-Range Graph Neural Networks). |
Lanning Wei; Zhiqiang He; Huan Zhao; Quanming Yao; |
| 61 | HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, on account of the existence of heterogeneity, HINs show distinct data characteristics and thus require different treatment. To bridge this gap, in this paper we investigate the representation learning on HINs with Graph Transformer, and propose a novel model named HINormer, which capitalizes on a larger-range aggregation mechanism for node representation learning. |
Qiheng Mao; Zemin Liu; Chenghao Liu; Jianling Sun; |
| 62 | Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the complexity and variety of heterophilic graphs, the critical challenge of heterophilic graph NAS mainly lies in developing the heterophily-specific search space and strategy. Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities. |
Xin Zheng; Miao Zhang; Chunyang Chen; Qin Zhang; Chuan Zhou; Shirui Pan; |
| 63 | Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method to utilize Counterfactual mechanism to generate artificial hard negative samples for Graph Contrastive learning, namely CGC. |
Haoran Yang; Hongxu Chen; Sixiao Zhang; Xiangguo Sun; Qian Li; Xiangyu Zhao; Guandong Xu; |
| 64 | Minimum Topology Attacks for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose an attack model, named MiBTack, based on a dynamic projected gradient descent algorithm, which can effectively solve the involving non-convex constraint optimization on discrete topology. |
Mengmei Zhang; Xiao Wang; Chuan Shi; Lingjuan Lyu; Tianchi Yang; Junping Du; |
| 65 | Multi-head Variational Graph Autoencoder Constrained By Sum-product Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel deep probabilistic model for graph analysis, termed Multi-head Variational Graph Autoencoder Constrained by Sum-product Networks (named SPN-MVGAE), which helps to relax the mean-field assumption and learns better latent representation with fault tolerance. |
Riting Xia; Yan Zhang; Chunxu Zhang; Xueyan Liu; Bo Yang; |
| 66 | GIF: A General Graph Unlearning Strategy Via Influence Function Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore the influence function tailored for graph unlearning, so as to improve the unlearning efficacy and efficiency for graph unlearning. |
Jiancan Wu; Yi Yang; Yuchun Qian; Yongduo Sui; Xiang Wang; Xiangnan He; |
| 67 | Learning Mixtures of Markov Chains with Quality Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work we contribute to the problem of unmixing Markov chains by highlighting and addressing two important constraints of the algorithm [10]: some chains in the mixture may not even be weakly connected, and secondly in practice one does not know beforehand the true number of chains. |
Fabian Spaeh; Charalampos Tsourakakis; |
| 68 | INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. |
Chuanpan Zheng; Xiaoliang Fan; Cheng Wang; Jianzhong Qi; Chaochao Chen; Longbiao Chen; |
| 69 | Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as �GNN Session-based New Item Recommendation (GSNIR)�. To solve this problem, we propose a dual-intent enhanced graph neural network for it. |
Di Jin; Luzhi Wang; Yizhen Zheng; Guojie Song; Fei Jiang; Xiang Li; Wei Lin; Shirui Pan; |
| 70 | Cut-matching Games for Generalized Hypergraph Ratio Cuts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present an O(log n)-approximation algorithm for a broad class of hypergraph ratio cut objectives. |
Nate Veldt; |
| 71 | Toward Degree Bias in Embedding-Based Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we validate the existence of degree bias in embedding-based KGC and identify the key factor to degree bias. |
Harry Shomer; Wei Jin; Wentao Wang; Jiliang Tang; |
| 72 | Unlearning Graph Classifiers with Limited Data Resources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our main contribution is the first known nonlinear approximate graph unlearning method based on GSTs. |
Chao Pan; Eli Chien; Olgica Milenkovic; |
| 73 | KGTrust: Evaluating Trustworthiness of SIoT Via Knowledge Enhanced Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the underlying structure of SIoT is usually complex, including both the heterogeneous graph structure and pairwise trust relationships, which renders hard to preserve the properties of SIoT trust during information propagation. To address these aforementioned problems, we propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT. |
Zhizhi Yu; Di Jin; Cuiying Huo; Zhiqiang Wang; Xiulong Liu; Heng Qi; Jia Wu; Lingfei Wu; |
| 74 | GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the performance of masked feature reconstruction naturally relies on the discriminability of the input features and is usually vulnerable to disturbance in the features. In this paper, we present a masked self-supervised learning framework1 GraphMAE2 with the goal of overcoming this issue. |
Zhenyu Hou; Yufei He; Yukuo Cen; Xiao Liu; Yuxiao Dong; Evgeny Kharlamov; Jie Tang; |
| 75 | CogDL: A Comprehensive Library for Graph Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The research and applications of graph deep learning present new challenges, including the sparse nature of graph data, complicated training of GNNs, and non-standard evaluation of graph tasks. To tackle the issues, we present CogDL1, a comprehensive library for graph deep learning that allows researchers and practitioners to conduct experiments, compare methods, and build applications with ease and efficiency. |
Yukuo Cen; Zhenyu Hou; Yan Wang; Qibin Chen; Yizhen Luo; Zhongming Yu; Hengrui Zhang; Xingcheng Yao; Aohan Zeng; Shiguang Guo; Yuxiao Dong; Yang Yang; Peng Zhang; Guohao Dai; Yu Wang; Chang Zhou; Hongxia Yang; Jie Tang; |
| 76 | ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing GNNs treat users and items equally and cannot distinguish diverse local patterns of each node, which makes them suboptimal in the recommendation scenario. To resolve this challenge, we present a node-wise adaptive graph neural network framework ApeGNN. |
Dan Zhang; Yifan Zhu; Yuxiao Dong; Yuandong Wang; Wenzheng Feng; Evgeny Kharlamov; Jie Tang; |
| 77 | Enhancing User Personalization in Conversational Recommenders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance user personalization in the interactive preference elicitation process by prioritizing attributes that most effectively represent the actual preference space of the user; and (ii) a user representation refiner, to effectively fuse together the user preferences collected from the interactive elicitation process to obtain a more personalized understanding of the user. |
Allen Lin; Ziwei Zhu; Jianling Wang; James Caverlee; |
| 78 | LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose LINet, a Location and Intention-aware neural Network for hotel group recommendation. |
Ruitao Zhu; Detao Lv; Yao Yu; Ruihao Zhu; Zhenzhe Zheng; Ke Bu; Quan Lu; Fan Wu; |
| 79 | Multi-Modal Self-Supervised Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a new Multi-Modal Self-Supervised Learning (MMSSL) method which tackles two key challenges. |
Wei Wei; Chao Huang; Lianghao Xia; Chuxu Zhang; |
| 80 | Distillation from Heterogeneous Models for Top-K Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a new KD framework, named HetComp, that guides the student model by transferring easy-to-hard sequences of knowledge generated from the teachers’ trajectories. |
SeongKu Kang; Wonbin Kweon; Dongha Lee; Jianxun Lian; Xing Xie; Hwanjo Yu; |
| 81 | On The Theories Behind Hard Negative Sampling for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we fill the research gap by conducting thorough theoretical analyses on HNS. |
Wentao Shi; Jiawei Chen; Fuli Feng; Jizhi Zhang; Junkang Wu; Chongming Gao; Xiangnan He; |
| 82 | Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency improving, and identify the potential item group structure in the sampled graph. |
Tianjun Wei; Jianghong Ma; Tommy W. S. Chow; |
| 83 | Exploration and Regularization of The Latent Action Space in Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the action space of the recommendation policy is a list of items, which could be extremely large with a dynamic candidate item pool. To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step. |
Shuchang Liu; Qingpeng Cai; Bowen Sun; Yuhao Wang; Ji Jiang; Dong Zheng; Peng Jiang; Kun Gai; Xiangyu Zhao; Yongfeng Zhang; |
| 84 | Bootstrap Latent Representations for Multi-modal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, existing multi-modal recommendation methods usually leverage randomly sampled negative examples in Bayesian Personalized Ranking (BPR) loss to guide the learning of user/item representations, which increases the computational cost on large graphs and may also bring noisy supervision signals into the training process. To tackle the above issues, we propose a novel self-supervised multi-modal recommendation model, dubbed BM3, which requires neither augmentations from auxiliary graphs nor negative samples. |
Xin Zhou; Hongyu Zhou; Yong Liu; Zhiwei Zeng; Chunyan Miao; Pengwei Wang; Yuan You; Feijun Jiang; |
| 85 | Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We argue that current KT modeling, as well as training paradigm, can lead to models tracing patterns of learner’s learning activities, instead of their evolving knowledge states. In this paper, we propose a new architecture, Diagnostic Transformer (DTransformer), along with a new training paradigm, to tackle this challenge. |
Yu Yin; Le Dai; Zhenya Huang; Shuanghong Shen; Fei Wang; Qi Liu; Enhong Chen; Xin Li; |
| 86 | Two-Stage Constrained Actor-Critic for Short Video Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate the problem of short video recommendation as a Constrained Markov Decision Process (CMDP). |
Qingpeng Cai; Zhenghai Xue; Chi Zhang; Wanqi Xue; Shuchang Liu; Ruohan Zhan; Xueliang Wang; Tianyou Zuo; Wentao Xie; Dong Zheng; Peng Jiang; Kun Gai; |
| 87 | Recommendation with Causality Enhanced Natural Language Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the effectiveness, existing models are mostly optimized based on the observed datasets, which can be skewed due to the selection or exposure bias. To alleviate this problem, in this paper, we formulate the task of explainable recommendation with a causal graph, and design a causality enhanced framework to generate unbiased explanations. |
Jingsen Zhang; Xu Chen; Jiakai Tang; Weiqi Shao; Quanyu Dai; Zhenhua Dong; Rui Zhang; |
| 88 | Cross-domain Recommendation Via User Interest Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, considering that their training objective is recommendation task-oriented without specific regularizations, the optimized embeddings disregard the interest alignment among user’s views, and even violate the user’s original interest distribution. To address these challenges, we propose a novel cross-domain recommendation framework, namely COAST, to improve recommendation performance on dual domains by perceiving the cross-domain similarity between entities and aligning user interests. |
Chuang Zhao; Hongke Zhao; Ming HE; Jian Zhang; Jianping Fan; |
| 89 | Robust Recommendation with Adversarial Gaussian Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, due to the extremely sparse user-item interactions, the learned recommender models can be less robust and sensitive to the highly dynamic user preferences and easily changed recommendation environments. To alleviate this problem, in this paper, we propose a simple yet effective robust recommender framework by generating additional samples from the Gaussian distributions. |
Zhenlei Wang; Xu Chen; |
| 90 | Learning to Simulate Daily Activities Via Modeling Dynamic Human Needs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, motivated by the classic psychological theory, Maslow’s need theory describing human motivation, we propose a knowledge-driven simulation framework based on generative adversarial imitation learning. |
Yuan Yuan; Huandong Wang; Jingtao Ding; Depeng Jin; Yong Li; |
| 91 | Dual-interest Factorization-heads Attention for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Dual-interest Factorization-heads Attention for Sequential Recommendation (short for DFAR) consisting of feedback-aware encoding layer, dual-interest disentangling layer and prediction layer. |
Guanyu Lin; Chen Gao; Yu Zheng; Jianxin Chang; Yanan Niu; Yang Song; Zhiheng Li; Depeng Jin; Yong Li; |
| 92 | Contrastive Collaborative Filtering for Cold-Start Item Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, since the training of the cold-start recommendation models is conducted on warm datasets, the existent methods face the issue that the collaborative embeddings of items will be blurred, which significantly degenerates the performance of cold-start item recommendation. To address this issue, we propose a novel model called Contrastive Collaborative Filtering for Cold-start item Recommendation (CCFCRec), which capitalizes on the co-occurrence collaborative signals in warm training data to alleviate the issue of blurry collaborative embeddings for cold-start item recommendation. |
Zhihui Zhou; Lilin Zhang; Ning Yang; |
| 93 | Anti-FakeU: Defending Shilling Attacks on Graph Neural Network Based Recommender Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we for the first time conduct a systematic study on the vulnerability of GNN based recommendation model against the shilling attack. |
Xiaoyu You; Chi Li; Daizong Ding; Mi Zhang; Fuli Feng; Xudong Pan; Min Yang; |
| 94 | Controllable Universal Fair Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We name this task universal fair representation learning, in which an exponential number of sensitive attributes need to be dealt with, bringing the challenges of unreasonable computational cost and un-guaranteed fairness constraints. To address these problems, we propose a controllable universal fair representation learning (CUFRL) method. |
Yue Cui; Ma Chen; Kai Zheng; Lei Chen; Xiaofang Zhou; |
| 95 | Compressed Interaction Graph Based Framework for Multi-behavior Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data “as features” and gradient conflict in multi-task learning when treating multi-behavior data “as labels”. In this paper, we propose CIGF, a Compressed Interaction Graph based Framework, to overcome the above limitations. |
Wei Guo; Chang Meng; Enming Yuan; Zhicheng He; Huifeng Guo; Yingxue Zhang; Bo Chen; Yaochen Hu; Ruiming Tang; Xiu Li; Rui Zhang; |
| 96 | A Counterfactual Collaborative Session-based Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we analyze the causalities and correlations of the OSCs in SBRSs from the perspective of causal inference. |
Wenzhuo Song; Shoujin Wang; Yan Wang; Kunpeng Liu; Xueyan Liu; Minghao Yin; |
| 97 | Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose , a hierarchical hypergraph network based correlative preference transfer framework for MDR, which represents multi-domain user-item interactions into a unified graph to help preference transfer. |
Zixuan Xu; Penghui Wei; Shaoguo Liu; Weimin Zhang; Liang Wang; Bo Zheng; |
| 98 | Automated Self-Supervised Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation. To fill this crucial gap, this work proposes a unified Automated Collaborative Filtering (AutoCF) to automatically perform data augmentation for recommendation. |
Lianghao Xia; Chao Huang; Chunzhen Huang; Kangyi Lin; Tao Yu; Ben Kao; |
| 99 | AutoDenoise: Automatic Data Instance Denoising for Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Deep Reinforcement Learning (DRL) based framework, AutoDenoise, with an Instance Denoising Policy Network, for denoising data instances with an instance selection manner in deep recommender systems. |
Weilin Lin; Xiangyu Zhao; Yejing Wang; Yuanshao Zhu; Wanyu Wang; |
| 100 | Improving Recommendation Fairness Via Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study how to improve recommendation fairness from the data augmentation perspective. |
Lei Chen; Le Wu; Kun Zhang; Richang Hong; Defu Lian; Zhiqiang Zhang; Jun Zhou; Meng Wang; |
| 101 | ColdNAS: Search to Modulate for User Cold-Start Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Instead of using a fixed modulation function and deciding modulation position by expertise, we propose a modulation framework called ColdNAS for user cold-start problem, where we look for proper modulation structure, including function and position, via neural architecture search. |
Shiguang Wu; Yaqing Wang; Qinghe Jing; Daxiang Dong; Dejing Dou; Quanming Yao; |
| 102 | AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To improve training and inference efficiency, we propose a Similarity-Structure Aware Shallow Autoencoder on top of three similarity structures, including Co-Occurrence, KNN and NSW. |
Rui Fan; Yuanhao Pu; Jin Chen; Zhihao Zhu; Defu Lian; Enhong Chen; |
| 103 | Quantize Sequential Recommenders Without Private Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework to quantize sequential recommenders without access to any real private data. |
Lingfeng Shi; Yuang Liu; Jun Wang; Wei Zhang; |
| 104 | Interaction-level Membership Inference Attack Against Federated Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate the interaction-level membership attack threats, we design a simple yet effective defense method to significantly reduce the attacker’s inference accuracy without losing recommendation performance. |
Wei Yuan; Chaoqun Yang; Quoc Viet Hung Nguyen; Lizhen Cui; Tieke He; Hongzhi Yin; |
| 105 | Debiased Contrastive Learning for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new Debiased Contrastive learning paradigm for Recommendation (DCRec) that unifies sequential pattern encoding with global collaborative relation modeling through adaptive conformity-aware augmentation. |
Yuhao Yang; Chao Huang; Lianghao Xia; Chunzhen Huang; Da Luo; Kangyi Lin; |
| 106 | Clustered Embedding Learning for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches either can only address one of the limitations or have flawed overall performances. In this paper, we propose Clustered Embedding Learning (CEL) as an integrated solution to these two problems. |
Yizhou Chen; Guangda Huzhang; Anxiang Zeng; Qingtao Yu; Hui Sun; Heng-Yi Li; Jingyi Li; Yabo Ni; Han Yu; Zhiming Zhou; |
| 107 | Adap-τ : Adaptively Modulating Embedding Magnitude for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fully foster the merits of the normalization while circumvent its limitation, this work studied on how to adaptively set the proper t. |
Jiawei Chen; Junkang Wu; Jiancan Wu; Xuezhi Cao; Sheng Zhou; Xiangnan He; |
| 108 | Robust Preference-Guided Denoising for Graph Based Social Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we instead propose to improve graph based social recommendation by only retaining the informative social relations to ensure an efficient and effective influence diffusion, i.e., graph denoising. |
Yuhan Quan; Jingtao Ding; Chen Gao; Lingling Yi; Depeng Jin; Yong Li; |
| 109 | MMMLP: Multi-modal Multilayer Perceptron For Sequential Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing sequential recommendation methods either fail to directly handle multi-modality or suffer from high computational complexity. To address this, we propose a novel Multi-Modal Multi-Layer Perceptron (MMMLP) for maintaining multi-modal sequences for sequential recommendation. |
Jiahao Liang; Xiangyu Zhao; Muyang Li; Zijian Zhang; Wanyu Wang; Haochen Liu; Zitao Liu; |
| 110 | Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose READER – a REsponse-Act guided reinforced Dialogue genERation model for the mental health counseling conversations. |
Aseem Srivastava; Ishan Pandey; Md Shad Akhtar; Tanmoy Chakraborty; |
| 111 | Few-shot News Recommendation Via Cross-lingual Transfer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge two domains that are even in different languages and without any overlapping users and news, we propose a novel unsupervised cross-lingual transfer model as the news encoder that aligns semantically similar news in two domains. |
Taicheng Guo; Lu Yu; Basem Shihada; Xiangliang Zhang; |
| 112 | User Retention-oriented Recommendation with Decision Transformer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation. In this work, we, therefore, contribute a series of strategies for tackling the exposed issues. |
Kesen Zhao; Lixin Zou; Xiangyu Zhao; Maolin Wang; Dawei Yin; |
| 113 | Cooperative Retriever and Ranker in Deep Recommenders Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present a novel framework for the joint training of retriever and ranker, named CoRR (Cooperative Retriever and Ranker). |
Xu Huang; Defu Lian; Jin Chen; Liu Zheng; Xing Xie; Enhong Chen; |
| 114 | Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite the promising transferability, the binding between item text and item representations might be too tight, leading to potential problems such as over-emphasizing the effect of text features and exaggerating the negative impact of domain gap. To address this issue, this paper proposes VQ-Rec, a novel approach to learning Vector-Quantized item representations for transferable sequential Recommenders. |
Yupeng Hou; Zhankui He; Julian McAuley; Wayne Xin Zhao; |
| 115 | Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current FRSs often overlook an important aspect when making FR, that is, the compatibility of the clothing item or outfit recommendations is highly dependent on the scene context. To this end, we propose the scene-aware fashion recommender system (SAFRS), which uncovers a hitherto unexplored avenue where scene information is taken into account when constructing the FR model. |
Tangwei Ye; Liang Hu; Qi Zhang; Zhong Yuan Lai; Usman Naseem; Dora D. Liu; |
| 116 | Multi-Behavior Recommendation with Cascading Graph Convolution Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). |
Zhiyong Cheng; Sai Han; Fan Liu; Lei Zhu; Zan Gao; Yuxin Peng; |
| 117 | AutoMLP: Automated MLP for Sequential Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users’ long/short-term interests from their historical interactions. |
Muyang Li; Zijian Zhang; Xiangyu Zhao; Wanyu Wang; Minghao Zhao; Runze Wu; Ruocheng Guo; |
| 118 | NASRec: Weight Sharing Neural Architecture Search for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose NASRec, a paradigm that trains a single supernet and efficiently produces abundant models/sub-architectures by weight sharing. |
Tunhou Zhang; Dehua Cheng; Yuchen He; Zhengxing Chen; Xiaoliang Dai; Liang Xiong; Feng Yan; Hai Li; Yiran Chen; Wei Wen; |
| 119 | Membership Inference Attacks Against Sequential Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, most MIA frameworks assume that they can obtain some in-distribution data from the same distribution of the target data, which is hard to gain in recommender system. To address these difficulties, we propose a Membership Inference Attack framework against sequential recommenders based on Model Extraction(ME-MIA). |
Zhihao Zhu; Chenwang Wu; Rui Fan; Defu Lian; Enhong Chen; |
| 120 | Offline Policy Evaluation in Large Action Spaces Via Outcome-Oriented Action Grouping Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Motivated by these, we propose a novel IPS estimator with outcome-oriented action Grouping (GroupIPS), which leverages a Lipschitz regularized network to measure the distance of action effects in the embedding space and merges nearest action neighbors. |
Jie Peng; Hao Zou; Jiashuo Liu; Shaoming Li; Yibao Jiang; Jian Pei; Peng Cui; |
| 121 | Communicative MARL-based Relevance Discerning Network for Repetition-Aware Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel Communicative MARL-based Relevance Discerning Network (CARDfor short) to automatically discern the item relevance for a better repetition-aware recommendation. |
Kaiyuan Li; Pengfei Wang; Haitao Wang; Qiang Liu; Xingxing Wang; Dong Wang; Shangguang Wang; |
| 122 | Invariant Collaborative Filtering to Popularity Distribution Shift Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel learning framework, Invariant Collaborative Filtering (InvCF), to discover disentangled representations that faithfully reveal the latent preference and popularity semantics without making any assumption about the popularity distribution. |
An Zhang; Jingnan Zheng; Xiang Wang; Yancheng Yuan; Tat-Seng Chua; |
| 123 | Modeling Temporal Positive and Negative Excitation for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose modeling both static interest and negative excitation for dynamic interest to further improve the recommendation performance. |
Chengkai Huang; Shoujin Wang; Xianzhi Wang; Lina Yao; |
| 124 | Personalized Graph Signal Processing for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a personalized graph signal processing (PGSP) method for collaborative filtering. |
Jiahao Liu; Dongsheng Li; Hansu Gu; Tun Lu; Peng Zhang; Li Shang; Ning Gu; |
| 125 | Multi-Task Recommendations with Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, balancing multiple objectives has always been a challenge in this field, which is typically avoided via linear estimations in existing works. To address these issues, in this paper, we propose a Reinforcement Learning (RL) enhanced MTL framework, namely RMTL, to combine the losses of different recommendation tasks using dynamic weights. |
Ziru Liu; Jiejie Tian; Qingpeng Cai; Xiangyu Zhao; Jingtong Gao; Shuchang Liu; Dayou Chen; Tonghao He; Dong Zheng; Peng Jiang; Kun Gai; |
| 126 | A Self-Correcting Sequential Recommender Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations. To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. |
Yujie Lin; Chenyang Wang; Zhumin Chen; Zhaochun Ren; Xin Xin; Qiang Yan; Maarten de Rijke; Xiuzhen Cheng; Pengjie Ren; |
| 127 | Cross-domain Recommendation with Behavioral Importance Perception Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the problem, we propose a generic behavioral importance-aware optimization framework for cross-domain recommendation (BIAO). |
Hong Chen; Xin Wang; Ruobing Xie; Yuwei Zhou; Wenwu Zhu; |
| 128 | Balancing Unobserved Confounding with A Few Unbiased Ratings in Debiased Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method with the aim of combating unobserved confounding and model misspecification. |
Haoxuan Li; Yanghao Xiao; Chunyuan Zheng; Peng Wu; |
| 129 | FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose FedACK, a new federated adversarial contrastive knowledge distillation framework for social bot detection. |
Yingguang Yang; Renyu Yang; Hao Peng; Yangyang Li; Tong Li; Yong Liao; Pengyuan Zhou; |
| 130 | Code Recommendation for Open Source Software Developers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects. |
Yiqiao Jin; Yunsheng Bai; Yanqiao Zhu; Yizhou Sun; Wei Wang; |
| 131 | Web Table Formatting Affects Readability on Mobile Devices Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For this work, we first conducted a survey to investigate how people interact with tables on mobile devices and conducted a study with designers to identify which design considerations are most critical. Based on these findings, we designed and conducted three large scale studies with remote crowdworker participants. |
Christopher Tensmeyer; Zoya Bylinski; Tianyuan Cai; Dave Miller; Ani Nenkova; Aleena Niklaus; Shaun Wallace; |
| 132 | Web Structure Derived Clustering for Optimised Web Accessibility Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To optimise the accessibility evaluation process, we aim to reduce the number of pages auditors must review by employing statistically representative pages, reducing a site of thousands of pages to a manageable review of archetypal pages. |
Alexander Hambley; Yeliz Yesilada; Markel Vigo; Simon Harper; |
| 133 | Denoising and Prompt-Tuning for Multi-Behavior Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, a large number of auxiliary behaviors (i.e., click and add-to-cart) could introduce irrelevant information to recommenders, which could mislead the target behavior (i.e., purchase) recommendation, rendering two critical challenges: (i) denoising auxiliary behaviors and (ii) bridging the semantic gap between auxiliary and target behaviors. Motivated by the above observation, we propose a novel framework–Denoising and Prompt-Tuning (DPT) with a three-stage learning paradigm to solve the aforementioned challenges. |
Chi Zhang; Rui Chen; Xiangyu Zhao; Qilong Han; Li Li; |
| 134 | PFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge the gap, we present pFedPrompt, which leverages the unique advantage of multimodality in vision-language models by learning user consensus from linguistic space and adapting to user characteristics in visual space in a non-parametric manner. |
Tao Guo; Song Guo; Junxiao Wang; |
| 135 | Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a novel self-supervised learning framework based on the Mutual WasserStein discrepancy minimization (MStein) for the sequential recommendation. |
Ziwei Fan; Zhiwei Liu; Hao Peng; Philip S Yu; |
| 136 | Confident Action Decision Via Hierarchical Policy Learning for Conversational Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, existing methods do not distinguish whether the past behaviors inferred from the historical interactions are closely related to the user’s current preference. To address these issues, we propose a novel Hierarchical policy learning based Conversational Recommendation framework (HiCR). |
Heeseon Kim; Hyeongjun Yang; Kyong-Ho Lee; |
| 137 | CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike these methods, in this paper, we focus on improving the recommendation performance for minority users of biased attributes. Along this line, we propose a novel attribute-aware Counterfactual Augmentation framework for Minority Users(CAMUS). |
Yuxin Ying; Fuzhen Zhuang; Yongchun Zhu; Deqing Wang; Hongwei Zheng; |
| 138 | Word Sense Disambiguation By Refining Target Word Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, only a few works model the correlation between the target context and gloss. We add to the body of literature by presenting a model that employs a multi-head attention mechanism on deep contextual features of the target word and candidate glosses to refine the target word embedding. |
Xuefeng Zhang; Richong Zhang; Xiaoyang Li; Fanshuang Kong; Junfan Chen; Samuel Mensah; Yongyi Mao; |
| 139 | Hashtag-Guided Low-Resource Tweet Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, training on tweets is challenging and demands large-scale human-annotated labels, which are time-consuming and costly to obtain. In this paper, we find that providing hashtags to social media tweets can help alleviate this issue because hashtags can enrich short and ambiguous tweets in terms of various information, such as topic, sentiment, and stance. |
Shizhe Diao; Sedrick Scott Keh; Liangming Pan; Zhiliang Tian; Yan Song; Tong Zhang; |
| 140 | HISum: Hyperbolic Interaction Model for Extractive Multi-Document Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new hyperbolic interaction model for extractive multi-document summarization (HISum). |
Mingyang Song; Yi Feng; Liping Jing; |
| 141 | FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it would incur severe issues, such as fixed scale representations, temporal-invariant and quadratic time complexity, with transformers directly applicable to the MTSC task because of the distinct properties of time series data. To tackle these issues, we propose FormerTime, an hierarchical representation model for improving the classification capacity for the MTSC task. |
Mingyue Cheng; Qi Liu; Zhiding Liu; Zhi Li; Yucong Luo; Enhong Chen; |
| 142 | Descartes: Generating Short Descriptions of Wikipedia Articles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nonetheless, a large fraction of articles (ranging from 10.2% in Dutch to 99.7% in Kazakh) have no short description yet, with detrimental effects for millions of Wikipedia users. Motivated by this problem, we introduce the novel task of automatically generating short descriptions for Wikipedia articles and propose Descartes, a multilingual model for tackling it. |
Marija Sakota; Maxime Peyrard; Robert West; |
| 143 | Dynamically Expandable Graph Convolution for Streaming Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing methods either rely on the historical data replay which is often not practical under increasingly strict data regulations, or can seldom solve the over-stability issue. To overcome these difficulties, we propose a novel Dynamically Expandable Graph Convolution (DEGC) algorithm from a model isolation perspective for the streaming recommendation which is orthogonal to previous methods. |
Bowei He; Xu He; Yingxue Zhang; Ruiming Tang; Chen Ma; |
| 144 | A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on improving DST module to generate dialogue states in circumstances with limited annotations and knowledge about slot ontology. |
Yuting Yang; Wenqiang Lei; Pei Huang; Juan Cao; Jintao Li; Tat-Seng Chua; |
| 145 | Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel reinforcement learning-based message passing framework for recommender systems, which we call DPAO (Dual Policy learning framework for Aggregation Optimization). |
Heesoo Jung; Sangpil Kim; Hogun Park; |
| 146 | CL-WSTC: Continual Learning for Weakly Supervised Text Classification on The Internet Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there are still no studies of applying WSTC methods in a continual learning paradigm to actually accommodate the open and evolving Internet. In this paper, we tackle this problem for the first time and propose a framework, named Continual Learning for Weakly Supervised Text Classification (CL-WSTC), which can take any WSTC method as base model. |
Miaomiao Li; Jiaqi Zhu; Xin Yang; Yi Yang; Qiang Gao; Hongan Wang; |
| 147 | TTS: A Target-based Teacher-Student Framework for Zero-Shot Stance Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The goal of zero-shot stance detection (ZSSD) is to identify the stance (in favor of, against, or neutral) of a text towards an unseen target in the inference stage. In this paper, we explore this problem from a novel angle by proposing a Target-based Teacher-Student learning (TTS) framework. |
Yingjie Li; Chenye Zhao; Cornelia Caragea; |
| 148 | Learning Robust Multi-Modal Representation for Multi-Label Emotion Recognition Via Adversarial Masking and Perturbation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These biases may lead to instability (e.g., performing poorly when the neglected modality is dominant for recognition) and weak generalization (e.g., performing poorly when unseen data is inconsistent with overfitted data) of the model on unseen data. To address these problems, this paper presents two adversarial training strategies to learn more robust multi-modal representation for multi-label emotion recognition. |
Shiping Ge; Zhiwei Jiang; Zifeng Cheng; Cong Wang; Yafeng Yin; Qing Gu; |
| 149 | Continual Few-shot Learning with Transformer Adaptation and Knowledge Regularization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Continual few-shot learning algorithm with Semantic knowledge Regularization (CoSR) for adapting to the distribution changes of visual prototypes through a Transformer-based prototype adaptation mechanism. |
Xin Wang; Yue Liu; Jiapei Fan; Weigao Wen; Hui Xue; Wenwu Zhu; |
| 150 | Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: From this perspective, we aim to address the heterophily problem in the spectral domain. |
Yuan Gao; Xiang Wang; Xiangnan He; Zhenguang Liu; Huamin Feng; Yongdong Zhang; |
| 151 | CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a new framework CTRLStruct for dialogue structure learning to effectively explore topic-level dialogue clusters as well as their transitions with unlabelled information. |
Congchi Yin; Piji Li; Zhaochun Ren; |
| 152 | KAE-Informer: A Knowledge Auto-Embedding Informer for Forecasting Long-Term Workloads of Microservices Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a knowledge auto-embedding Informer network (KAE-Informer) for forecasting the long-term QPS sequences of microservices. |
Qin Hua; Dingyu Yang; Shiyou Qian; Hanwen Hu; Jian Cao; Guangtao Xue; |
| 153 | Open-World Social Event Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In practice, new events on Internet may not belong to any existing/seen class, and therefore cannot be correctly identified by closed-world learning algorithms. To tackle these challenges, we propose an Open-World Social Event Classifier (OWSEC) model in this paper. |
Shengsheng Qian; Hong Chen; Dizhan Xue; Quan Fang; Changsheng Xu; |
| 154 | KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance. Based on this observation, we propose a novel knowledge-aware approach to political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) to learn the relationships among words and sentences in three different levels and (2) knowledge encoding (KE) to incorporate external knowledge for real-world entities into the process of political stance prediction. |
Yunyong Ko; Seongeun Ryu; Soeun Han; Youngseung Jeon; Jaehoon Kim; Sohyun Park; Kyungsik Han; Hanghang Tong; Sang-Wook Kim; |
| 155 | Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Social relation information can play an assistant role in the (dis)agreement task besides textual information. We propose a novel method to extract such relation information from (dis)agreement data into an inductive social relation graph, merely using the comment-reply pairs without any additional platform-specific information. |
Yun Luo; Zihan Liu; Stan Z. Li; Yue Zhang; |
| 156 | Self-training Through Classifier Disagreement for Cross-Domain Opinion Target Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. |
Kai Sun; Richong Zhang; Mensah Samuel; Aletras Nikolaos; Yongyi Mao; Xudong Liu; |
| 157 | Dynalogue: A Transformer-Based Dialogue System with Dynamic Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to develop a cybersecurity-oriented dialogue system, called Dynalogue1, which can provide consultancy online as a cyber professional. |
Rongjunchen Zhang; Tingmin Wu; Xiao Chen; Sheng Wen; Surya Nepal; Cecile Paris; Yang Xiang; |
| 158 | Active Learning from The Web Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose an efficient method, Seafaring, to retrieve informative data in terms of active learning from the Web using a user-side information retrieval algorithm. |
Ryoma Sato; |
| 159 | The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. |
Yu Zhang; Bowen Jin; Qi Zhu; Yu Meng; Jiawei Han; |
| 160 | Fast and Multi-aspect Mining of Complex Time-stamped Event Streams Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our answer is to focus on two types of patterns, i.e., “regimes” and “components”, over high-order tensor streams, for which we present an efficient and effective method, namely CubeScope. |
Kota Nakamura; Yasuko Matsubara; Koki Kawabata; Yuhei Umeda; Yuichiro Wada; Yasushi Sakurai; |
| 161 | PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a new summarization problem, Evolving Multi-Document sets stream Summarization (EMDS), and introduce a novel unsupervised algorithm PDSum with the idea of prototype-driven continuous summarization. |
Susik Yoon; Hou Pong Chan; Jiawei Han; |
| 162 | “Why Is This Misleading?”: Detecting News Headline Hallucinations with Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the growing need for news headline generation, we argue that the hallucination issue, namely the generated headlines being not supported by the original news stories, is a critical challenge for the deployment of this feature in web-scale systems Meanwhile, due to the infrequency of hallucination cases and the requirement of careful reading for raters to reach the correct consensus, it is difficult to acquire a large dataset for training a model to detect such hallucinations through human curation. In this work, we present a new framework named ExHalder to address this challenge for headline hallucination detection. |
Jiaming Shen; Jialu Liu; Dan Finnie; Negar Rahmati; Mike Bendersky; Marc Najork; |
| 163 | DIWIFT: Discovering Instance-wise Influential Features for Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first introduce a new perspective based on the influence function for instance-wise feature selection, and give some corresponding theoretical insights, the core of which is to use the influence function as an indicator to measure the importance of an instance-wise feature. We then propose a new solution for discovering instance-wise influential features in tabular data (DIWIFT), where a self-attention network is used as a feature selection model and the value of the corresponding influence function is used as an optimization objective to guide the model. |
Dugang Liu; Pengxiang Cheng; Hong Zhu; Xing Tang; Yanyu Chen; Xiaoting Wang; Weike Pan; Zhong Ming; Xiuqiang He; |
| 164 | Learning Structural Co-occurrences for Structured Web Data Extraction in Low-Resource Settings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we further observe structural co-occurrences in different webpages of the same website: the same position in the DOM tree usually plays the same semantic role, and the DOM nodes in this position also share similar surface forms. Motivated by this, we propose a novel method, Structor, to effectively incorporate the structural co-occurrences over DOM tree and surface form into pre-trained language models. |
Zhenyu Zhang; Bowen Yu; Tingwen Liu; Tianyun Liu; Yubin Wang; Li Guo; |
| 165 | Learning Disentangled Representation Via Domain Adaptation for Dialogue Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to use a disentangled representation method to reduce the deviation between data in different domains, where the input data is disentangled into domain-invariant and domain-specific representations. |
Jinpeng Li; Yingce Xia; Xin Cheng; Dongyan Zhao; Rui Yan; |
| 166 | XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, in this work, we propose XWikiGen, which is the task of cross-lingual multi-document summarization of text from multiple reference articles, written in various languages, to generate Wikipedia-style text. |
Dhaval Taunk; Shivprasad Sagare; Anupam Patil; Shivansh Subramanian; Manish Gupta; Vasudeva Varma; |
| 167 | TMMDA: A New Token Mixup Multimodal Data Augmentation for Multimodal Sentiment Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To preserve semantics during virtual modality generation, we propose a novel cross-modal token mixup strategy based on the generative adversarial network. |
Xianbing Zhao; Yixin Chen; Sicen Liu; Xuan Zang; Yang Xiang; Buzhou Tang; |
| 168 | Node-wise Diffusion for Scalable Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, (i) labeled nodes involved in model training usually account for a small portion of graphs in the semi-supervised setting, and (ii) different nodes locate at different graph local contexts and it inevitably degrades the representation qualities if treating them undistinguishedly in diffusion. To address the above issues, we develop NDM, a universal node-wise diffusion model, to capture the unique characteristics of each node in diffusion, by which NDM is able to yield high-quality node representations. |
Keke Huang; Jing Tang; Juncheng Liu; Renchi Yang; Xiaokui Xiao; |
| 169 | BlinkViz: Fast and Scalable Approximate Visualization on Very Large Datasets Using Neural-Enhanced Mixed Sum-Product Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a learning-based visualization approach called BlinkViz, which uses a learned model to produce approximate visualizations by leveraging mixed sum-product networks to learn the distribution of the original data. |
Yimeng Qiao; Yinan Jing; Hanbing Zhang; Zhenying He; Kai Zhang; X. Sean Wang; |
| 170 | MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose MetaTroll, a text-based troll detection model based on the meta-learning framework that enables high portability and parameter-efficient adaptation to new campaigns using only a handful of labelled samples for few-shot transfer. |
Lin Tian; Xiuzhen Zhang; Jey Han Lau; |
| 171 | EmpMFF: A Multi-factor Sequence Fusion Framework for Empathetic Response Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, existing empathy models overvalue the empathy of responses while ignoring contextual relevance, which results in repetitive model-generated responses. To address these issues, we propose a Multi-Factor sequence Fusion framework (EmpMFF) based on conditional variational autoencoder. |
Xiaobing Pang; Yequan Wang; Siqi Fan; Lisi Chen; Shuo Shang; Peng Han; |
| 172 | Automatic Feature Selection By One-Shot Neural Architecture Search In Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, multiple features with high relevance may be selected simultaneously, resulting in sub-optimal result. In this work, we solve this problem by proposing an AutoML-based feature selection framework that can automatically search the optimal feature subset. |
He Wei; Yuekui Yang; Haiyang Wu; Yangyang Tang; Meixi Liu; Jianfeng Li; |
| 173 | Towards Understanding Consumer Healthcare Questions on The Web with Semantically Enhanced Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a semantically-enhanced contrastive learning-based framework for generating abstractive question summaries that are faithful and factually correct. |
Shweta Yadav; Ștefan Cobeli; Cornelia Caragea; |
| 174 | CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the effectiveness, most existing deep text clustering methods rely heavily on representations pre-trained in general domains, which may not be the most suitable solution for clustering in specific target domains. To address this issue, we propose CEIL, a novel Classification-Enhanced Iterative Learning framework for short text clustering, which aims at generally promoting the clustering performance by introducing a classification objective to iteratively improve feature representations. |
Mingjun Zhao; Mengzhen Wang; Yinglong Ma; Di Niu; Haijiang Wu; |
| 175 | Modeling Dynamic Interactions Over Tensor Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To model the shifting trends via interactions, namely dynamic interactions over tensor streams, in this paper, we propose a streaming algorithm, DISMO, that we designed to discover Dynamic Interactions and Seasonality in a Multi-Order tensor. |
Koki Kawabata; Yasuko Matsubara; Yasushi Sakurai; |
| 176 | Semi-supervised Adversarial Learning for Complementary Item Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i.e., for items for which no co-purchase statistics yet exist. |
Koby Bibas; Oren Sar Shalom; Dietmar Jannach; |
| 177 | Interval-censored Transformer Hawkes: Detecting Information Operations Using The Reaction of Social Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an Interval-censored Transformer Hawkes (IC-TH) architecture and a novel data encoding scheme to account for both observed and missing data. |
Quyu Kong; Pio Calderon; Rohit Ram; Olga Boichak; Marian-Andrei Rizoiu; |
| 178 | Constrained Subset Selection from Data Streams for Profit Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider a generalized k-system constraint, which captures various requirements in real-world applications. |
Shuang Cui; Kai Han; Jing Tang; He Huang; |
| 179 | Towards Model Robustness: Generating Contextual Counterfactuals for Entities in Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we formulate the problem of automatically generating CAD for RE tasks from an entity-centric viewpoint, and develop a novel approach to derive contextual counterfactuals for entities. |
Mi Zhang; Tieyun Qian; Ting Zhang; Xin Miao; |
| 180 | CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fill that gap, we propose a novel citation-aware scientific paper summarization framework based on the citation graph, able to accurately locate and incorporate the salient contents from references, as well as capture varying relevance between source papers and their references. |
Zheheng Luo; Qianqian Xie; Sophia Ananiadou; |
| 181 | SCStory: Self-supervised and Continual Online Story Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a framework SCStory for online story discovery, that helps people digest rapidly published news article streams in real-time without human annotations. |
Susik Yoon; Yu Meng; Dongha Lee; Jiawei Han; |
| 182 | Set in Stone: Analysis of An Immutable Web3 Social Media Platform Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We gather data for 24K users, 317K posts, 2.57M user actions, which have facilitated $6.75M worth of transactions. |
Wenrui Zuo; Aravindh Raman; Raul J Mondragón; Gareth Tyson; |
| 183 | Show Me Your NFT and I Tell You How It Will Perform: Multimodal Representation Learning for NFT Selling Price Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the NFT financial performance prediction has not been widely explored to date. In this work, we address the above problem based on the hypothesis that NFT images and their textual descriptions are essential proxies to predict the NFT selling prices. |
Davide Costa; Lucio La Cava; Andrea Tagarelli; |
| 184 | Catch: Collaborative Feature Set Search for Automated Feature Engineering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thanks to the rapid advances in reinforcement learning, it has offered an automated alternative, i.e. automated feature engineering (AutoFE). In this work, through scrutiny of the prior AutoFE methods, we characterize several research challenges that remained in this regime, concerning system-wide efficiency, efficacy, and practicality toward production. |
Guoshan Lu; Haobo Wang; Saisai Yang; Jing Yuan; Guozheng Yang; Cheng Zang; Gang Chen; Junbo Zhao; |
| 185 | CoTel: Ontology-Neural Co-Enhanced Text Labeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study how to utilize the rule-based and learning-based methods for resource-effective text labeling. |
Miao-Hui Song; Lan Zhang; Mu Yuan; Zichong Li; Qi Song; Yijun Liu; Guidong Zheng; |
| 186 | Extracting Cultural Commonsense Knowledge at Scale Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents Candle, an end-to-end methodology for extracting high-quality cultural commonsense knowledge (CCSK) at scale. |
Tuan-Phong Nguyen; Simon Razniewski; Aparna Varde; Gerhard Weikum; |
| 187 | Know Your Transactions: Real-time and Generic Transaction Semantic Representation on Blockchain & Web3 Ecosystem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Motif-based Transaction Semantics representation method (MoTS), which can capture the transaction semantic information in the real-time transaction data workflow. |
Zhiying Wu; Jieli Liu; Jiajing Wu; Zibin Zheng; Xiapu Luo; Ting Chen; |
| 188 | Toward Open-domain Slot Filling Via Self-supervised Co-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, these learning paradigms are significantly inferior to supervised learning approaches in terms of performance. To minimize this performance gap and demonstrate the possibility of open-domain slot filling, we propose a Self-supervised Co-training framework, called , that requires zero in-domain manually labeled training examples and works in three phases. |
Adib Mosharrof; Moghis Fereidouni; A.B. Siddique; |
| 189 | A Multi-view Meta-learning Approach for Multi-modal Response Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Further, similar to the dialogue model with plain text as input and output, the generated responses from multi-modal dialogue also lack diversity and informativeness. In this paper, to address the above issues, we propose a Multi-View Meta-Learning (MultiVML) algorithm that groups samples in multiple views and customizes generation models to different groups. |
Zhiliang Tian; Zheng Xie; Fuqiang Lin; Yiping Song; |
| 190 | Unsupervised Event Chain Mining from Multiple Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Massive and fast-evolving news articles keep emerging on the web. To effectively summarize and provide concise insights into real-world events, we propose a new event knowledge extraction task Event Chain Mining in this paper. |
Yizhu Jiao; Ming Zhong; Jiaming Shen; Yunyi Zhang; Chao Zhang; Jiawei Han; |
| 191 | Interactive Log Parsing Via Light-weight User Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper develops a human-in-the-loop template mining framework to support interactive log analysis, which is highly desirable in real-world diagnosis or troubleshooting of Web applications but yet previous template mining algorithms fail to support it. We formulate three types of light-weight user feedback and based on them we design three atomic human-in-the-loop template mining algorithms. |
Liming Wang; Hong Xie; Ye Li; Jian Tan; John C.S. Lui; |
| 192 | Measuring and Evading Turkmenistan’s Internet Censorship: A Case Study in Large-Scale Measurements of A Low-Penetration Country Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present the largest measurement study to date of Turkmenistan’s Web censorship. |
Sadia Nourin; Van Tran; Xi Jiang; Kevin Bock; Nick Feamster; Nguyen Phong Hoang; Dave Levin; |
| 193 | Provenance of Training Without Training Data: Towards Privacy-Preserving DNN Model Ownership Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Provenance of Training (PoT) scheme, the first empirical study towards verifying DNN model ownership without accessing any original dataset while being robust against existing attacks. |
Yunpeng Liu; Kexin Li; Zhuotao Liu; Bihan Wen; Ke Xu; Weiqiang Wang; Wenbiao Zhao; Qi Li; |
| 194 | Efficient and Low Overhead Website Fingerprinting Attacks and Defenses Based on TCP/IP Traffic Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we first propose a practical filter-assisted attack against RPD, which can filter out the injected noises using the statistical characteristics of TCP/IP traffic. Then, we propose a list-assisted defensive mechanism to defend the proposed attack method. |
Guodong Huang; Chuan Ma; Ming Ding; Yuwen Qian; Chunpeng Ge; Liming Fang; Zhe Liu; |
| 195 | MaSS: Model-agnostic, Semantic and Stealthy Data Poisoning Attack on Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we consider a more rigorous setting and propose a model-agnostic, semantic, and stealthy data poisoning attack on KGE models from a practical perspective. |
Xiaoyu You; Beina Sheng; Daizong Ding; Mi Zhang; Xudong Pan; Min Yang; Fuli Feng; |
| 196 | Curriculum Graph Poisoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we revisit the bi-level optimization problem in graph poisoning and propose a novel graph poisoning method, termed Curriculum Graph Poisoning (CuGPo), inspired by curriculum learning. |
Hanwen Liu; Peilin Zhao; Tingyang Xu; Yatao Bian; Junzhou Huang; Yuesheng Zhu; Yadong Mu; |
| 197 | On How Zero-Knowledge Proof Blockchain Mixers Improve, and Worsen User Privacy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We find that ZKP mixers are tightly intertwined with the growing number of Decentralized Finance (DeFi) attacks and Blockchain Extractable Value (BEV) extractions. |
Zhipeng Wang; Stefanos Chaliasos; Kaihua Qin; Liyi Zhou; Lifeng Gao; Pascal Berrang; Benjamin Livshits; Arthur Gervais; |
| 198 | Transferring Audio Deepfake Detection Capability Across Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper conducts the first comprehensive study on the cross-lingual perspective of deepfake detection. |
Zhongjie Ba; Qing Wen; Peng Cheng; Yuwei Wang; Feng Lin; Li Lu; Zhenguang Liu; |
| 199 | NetGuard: Protecting Commercial Web APIs from Model Inversion Attacks Using GAN-generated Fake Samples Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we proposed NetGuard, a novel utility-aware defense methodology against model inversion attacks (MIAs). |
Xueluan Gong; Ziyao Wang; Yanjiao Chen; Qian Wang; Cong Wang; Chao Shen; |
| 200 | Web Photo Source Identification Based on Neural Enhanced Camera Fingerprint Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents an innovative and practical source identification framework that employs neural-network enhanced sensor pattern noise to trace back web photos efficiently while ensuring security. |
Feng Qian; Sifeng He; Honghao Huang; Huanyu Ma; Xiaobo Zhang; Lei Yang; |
| 201 | TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the existing methods only extract flow-level features, failing to handle short flows because of unreliable statistical properties, or treat the header and payload equally, failing to mine the potential correlation between bytes. Therefore, in this paper, we propose a byte-level traffic graph construction approach based on point-wise mutual information (PMI), and a model named Temporal Fusion Encoder using Graph Neural Networks (TFE-GNN) for feature extraction. |
Haozhen Zhang; Le Yu; Xi Xiao; Qing Li; Francesco Mercaldo; Xiapu Luo; Qixu Liu; |
| 202 | Time-manipulation Attack: Breaking Fairness Against Proof of Authority Aura Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide a rigorous analysis of Aura. |
Xinrui Zhang; Rujia Li; Qin Wang; Qi Wang; Sisi Duan; |
| 203 | Meteor: Improved Secure 3-Party Neural Network Inference with Reducing Online Communication Costs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present Meteor, an online communication-efficient and fast secure 3-party computation neural network inference system aginst semi-honest adversary in honest-majority. |
Ye Dong; Chen Xiaojun; Weizhan Jing; Li Kaiyun; Weiping Wang; |
| 204 | Do NFTs’ Owners Really Possess Their Assets? A First Look at The NFT-to-Asset Connection Fragility Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, this paper aims to answer the question: Is the NFT-to-Asset connection fragile? |
Ziwei Wang; Jiashi Gao; Xuetao Wei; |
| 205 | Preserving Missing Data Distribution in Synthetic Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose several methods to generate synthetic data that preserve both the observable and the missing data distributions. |
Xinyue Wang; Hafiz Asif; Jaideep Vaidya; |
| 206 | Ginver: Generative Model Inversion Attacks Against Collaborative Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Compared to offloading the entire model to the cloud, the collaborative inference approach is more data privacy-preserving as the owner�s model input is not exposed to outsiders. However, we show in this paper that the adversary can restore the victim�s model input by exploiting the output of the victim�s local model. |
Yupeng Yin; Xianglong Zhang; Huanle Zhang; Feng Li; Yue Yu; Xiuzhen Cheng; Pengfei Hu; |
| 207 | The Hitchhiker’s Guide to Facebook Web Tracking with Invisible Pixels and Click IDs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We find that FB Pixel tracks a wide range of user activities on websites with alarming detail, especially on websites classified as sensitive categories under GDPR. Also, we show how the FBCLID tag can be used to match, and thus de-anonymize, activities of online users performed in the distant past (even before those users had a FB account) tracked by FB Pixel. |
Paschalis Bekos; Panagiotis Papadopoulos; Evangelos P. Markatos; Nicolas Kourtellis; |
| 208 | All Your Shops Are Belong to Us: Security Weaknesses in E-commerce Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we design and implement a security evaluation framework to uncover security vulnerabilities in e-commerce operations beyond checkout/payment integration. |
Rohan Pagey; Mohammad Mannan; Amr Youssef; |
| 209 | An Empirical Study of The Usage of Checksums for Web Downloads Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Checksums, typically provided on webpages and generated from cryptographic hash functions (e.g., MD5, SHA256) or signature schemes (e.g., PGP), are commonly used on websites to enable users to verify that the files they download have not been tampered with when stored on possibly untrusted servers. In this paper, we elucidate the current practices regarding the usage of checksums for web downloads (hash functions used, visibility and validity of checksums, type of websites and files, etc.), as this has been mostly overlooked so far. |
Gaël Bernard; Rémi Coudert; Bertil Chapuis; Kévin Huguenin; |
| 210 | Not Seen, Not Heard in The Digital World! Measuring Privacy Practices in Children’s Apps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, while legislatures around the world have enacted regulations to protect children’s online privacy, and app stores have instituted various protections, privacy in mobile apps remains a growing concern for parents and wider society. In this paper, we explore the potential privacy issues and threats that exist in these apps. |
Ruoxi Sun; Minhui Xue; Gareth Tyson; Shuo Wang; Seyit Camtepe; Surya Nepal; |
| 211 | Automatic Discovery of Emerging Browser Fingerprinting Techniques Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel hybrid system, named BFAD, that automatically identifies previously unknown browser fingerprinting APIs in the wild. |
Junhua Su; Alexandros Kapravelos; |
| 212 | BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneous Ethereum transactions. To address these challenges, we propose BERT4ETH, a universal pre-trained Transformer encoder that serves as an account representation extractor for detecting various fraud behaviors on Ethereum. |
Sihao Hu; Zhen Zhang; Bingqiao Luo; Shengliang Lu; Bingsheng He; Ling Liu; |
| 213 | Training-free Lexical Backdoor Attacks on Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose Training-Free Lexical Backdoor Attack (TFLexAttack) as the first training-free backdoor attack on language models. |
Yujin Huang; Terry Yue Zhuo; Qiongkai Xu; Han Hu; Xingliang Yuan; Chunyang Chen; |
| 214 | The Benefits of Vulnerability Discovery and Bug Bounty Programs: Case Studies of Chromium and Firefox Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a data-driven study of the Chromium and Firefox vulnerability-reward programs. |
Soodeh Atefi; Amutheezan Sivagnanam; Afiya Ayman; Jens Grossklags; Aron Laszka; |
| 215 | Cross-Modality Mutual Learning for Enhancing Smart Contract Vulnerability Detection on Bytecode Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel cross-modality mutual learning framework for enhancing smart contract vulnerability detection on bytecode. |
Peng Qian; Zhenguang Liu; Yifang Yin; Qinming He; |
| 216 | Net-track: Generic Web Tracking Detection Using Packet Metadata Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose Net-track, a novel approach to managing a secure web environment through platform-independent, encryption-agnostic detection of trackers. |
Dongkeun Lee; Minwoo Joo; Wonjun Lee; |
| 217 | The Chameleon on The Web: An Empirical Study of The Insidious Proactive Web Defacements Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we uncover proactive web defacements, where the involved web pages (i.e., landing pages) proactively deface themselves within browsers using JavaScript (i.e., control scripts). |
Rui Zhao; |
| 218 | Shield: Secure Allegation Escrow System with Stronger Guarantees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Having identified the attacks and issues in all prior works, we put forth an SAE system that overcomes these while retaining all the existing salient features. |
Nishat Koti; Varsha Bhat Kukkala; Arpita Patra; Bhavish Raj Gopal; |
| 219 | Unnoticeable Backdoor Attacks on Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Though there are some initial efforts in graph backdoor attacks, our empirical analysis shows that they may require a large attack budget for effective backdoor attacks and the injected triggers can be easily detected and pruned. Therefore, in this paper, we study a novel problem of unnoticeable graph backdoor attacks with limited attack budget. |
Enyan Dai; Minhua Lin; Xiang Zhang; Suhang Wang; |
| 220 | Bad Apples: Understanding The Centralized Security Risks in Decentralized Ecosystems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we systematically study the centralized security risks existing in decentralized ecosystems. |
Kailun Yan; Jilian Zhang; Xiangyu Liu; Wenrui Diao; Shanqing Guo; |
| 221 | Scan Me If You Can: Understanding and Detecting Unwanted Vulnerability Scanning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we design a testbed to characterize web vulnerability scanners using browser-based and network-based fingerprinting techniques. |
Xigao Li; Babak Amin Azad; Amir Rahmati; Nick Nikiforakis; |
| 222 | The More Things Change, The More They Stay The Same: Integrity of Modern JavaScript Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present key insights to inform the design of robust integrity mechanisms, derived from our large-scale analyses of the 6M scripts we collected while crawling 44K domains every day for 77 days. |
Johnny So; Michael Ferdman; Nick Nikiforakis; |
| 223 | Quantifying and Defending Against Privacy Threats on Federated Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct the first holistic study of the privacy threat on FKGE from both attack and defense perspectives. |
Yuke Hu; Wei Liang; Ruofan Wu; Kai Xiao; Weiqiang Wang; Xiaochen Li; Jinfei Liu; Zhan Qin; |
| 224 | AppSniffer: Towards Robust Mobile App Fingerprinting Against VPN Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we find that the performance of the existing mobile application fingerprinting systems significantly degrades when a virtual private network (VPN) is used. To address such a shortcoming, we propose a framework dubbed AppSniffer that uses a two-stage classification process for mobile app fingerprinting. |
Sanghak Oh; Minwook Lee; Hyunwoo Lee; Elisa Bertino; Hyoungshick Kim; |
| 225 | RICC: Robust Collective Classification of Sybil Accounts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose RICC, the first robust collective classification framework, designed to identify adversarial Sybil accounts created by adversarial attacks. |
Dongwon Shin; Suyoung Lee; Sooel Son; |
| 226 | IRWArt: Levering Watermarking Performance for Protecting High-quality Artwork Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To provide high-level protection for valuable artwork images, we propose a novel invisible robust watermarking framework, dubbed as IRWArt. |
Yuanjing Luo; Tongqing Zhou; Fang Liu; Zhiping Cai; |
| 227 | Sanitizing Sentence Embeddings (and Labels) for Local Differential Privacy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show how to achieve metric-based local DP (LDP) by sanitizing (high-dimensional) sentence embedding, extracted by LMs and much smaller than gradients. |
Minxin Du; Xiang Yue; Sherman S. M. Chow; Huan Sun; |
| 228 | ZTLS: A DNS-based Approach to Zero Round Trip Delay in TLS Handshake Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ZTLS to eliminate the 1-RTT latency for the TLS handshake by leveraging the DNS. |
Sangwon Lim; Hyeonmin Lee; Hyunsoo Kim; Hyunwoo Lee; Taekyoung Kwon; |
| 229 | AgrEvader: Poisoning Membership Inference Against Byzantine-robust Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we conduct a benchmark evaluation to assess the performance of PMIA against the Byzantine-robust FL setting that is specifically designed to mitigate poisoning attacks. |
Yanjun Zhang; Guangdong Bai; Mahawaga Arachchige Pathum Chamikara; Mengyao Ma; Liyue Shen; Jingwei Wang; Surya Nepal; Minhui Xue; Long Wang; Joseph Liu; |
| 230 | Event Prediction Using Case-Based Reasoning Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a case-based reasoning model, EvCBR, to predict properties about new consequent events based on similar cause-effect events present in the KG. |
Sola Shirai; Debarun Bhattacharjya; Oktie Hassanzadeh; |
| 231 | CapEnrich: Enriching Caption Semantics for Web Images Via Cross-modal Pre-trained Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: With no need for additional human annotations, we propose a plug-and-play framework, i.e CapEnrich, to complement the generic image descriptions with more semantic details. |
Linli Yao; Weijing Chen; Qin Jin; |
| 232 | Wikidata As A Seed for Web Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The volume and complexity of the data make this task difficult and time-consuming. In this work, we present a framework that is able to identify and extract new facts that are published under multiple Web domains so that they can be proposed for validation by Wikidata editors. |
Kunpeng Guo; Dennis Diefenbach; Antoine Gourru; Christophe Gravier; |
| 233 | Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most of the existing methods are incapable of explicitly modeling the long-term time dependencies from history and neglect the adaptive integration of the long- and short-term information. To tackle these problems, we propose a novel method that utilizes a designed Hierarchical Relational Graph Neural Network to learn the Long- and Short-term representations for TKG reasoning, namely HGLS. |
Mengqi Zhang; Yuwei Xia; Qiang Liu; Shu Wu; Liang Wang; |
| 234 | MLN4KB: An Efficient Markov Logic Network Engine for Large-scale Knowledge Bases and Structured Logic Rules Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, by focusing on a certain class of first-order logic rules that are sufficiently expressive, we develop a highly efficient MLN inference engine called MLN4KB that can leverage the sparsity of knowledge bases. |
Huang Fang; Yang Liu; Yunfeng Cai; Mingming Sun; |
| 235 | Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Meta-Learning based Temporal Knowledge Graph Extrapolation (MTKGE) model, which is trained on link prediction tasks sampled from the existing TKGs and tested in the emerging TKGs with unseen entities and relations. |
Zhongwu Chen; Chengjin Xu; Fenglong Su; Zhen Huang; Yong Dou; |
| 236 | Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose FedLU, a novel FL framework for heterogeneous KG embedding learning and unlearning. |
Xiangrong Zhu; Guangyao Li; Wei Hu; |
| 237 | Can Persistent Homology Provide An Efficient Alternative for Evaluation of Knowledge Graph Completion Methods? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we present a novel method, Knowledge Persistence (), for faster evaluation of Knowledge Graph (KG) completion approaches. |
Anson Bastos; Kuldeep Singh; Abhishek Nadgeri; Johannes Hoffart; Manish Singh; Toyotaro Suzumura; |
| 238 | A Single Vector Is Not Enough: Taxonomy Expansion Via Box Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose to project taxonomy entities into boxes (i.e., hyperrectangles). |
Song Jiang; Qiyue Yao; Qifan Wang; Yizhou Sun; |
| 239 | Knowledge Graph Question Answering with Ambiguous Query Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose PReFNet which focuses on answering ambiguous queries with pseudo relevance feedback on knowledge graphs. |
Lihui Liu; Yuzhong Chen; Mahashweta Das; Hao Yang; Hanghang Tong; |
| 240 | Atrapos: Real-time Evaluation of Metapath Query Workloads Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Atrapos, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. |
Serafeim Chatzopoulos; Thanasis Vergoulis; Dimitrios Skoutas; Theodore Dalamagas; Christos Tryfonopoulos; Panagiotis Karras; |
| 241 | Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge. |
Qian Li; Shu Guo; Yangyifei Luo; Cheng Ji; Lihong Wang; Jiawei Sheng; Jianxin Li; |
| 242 | TaxoComplete: Self-Supervised Taxonomy Completion Leveraging Position-Enhanced Semantic Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose TaxoComplete, a self-supervised taxonomy completion framework that learns the representation of nodes leveraging their position in the taxonomy. |
Ines Arous; Ljiljana Dolamic; Philippe Cudré-Mauroux; |
| 243 | Hierarchy-Aware Multi-Hop Question Answering Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose HamQA, a novel Hierarchy-aware multi-hop Question Answering framework on knowledge graphs, to effectively align the mutual hierarchical information between question contexts and KGs. |
Junnan Dong; Qinggang Zhang; Xiao Huang; Keyu Duan; Qiaoyu Tan; Zhimeng Jiang; |
| 244 | Unsupervised Entity Alignment for Temporal Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present DualMatch that effectively fuses the relational and temporal information for EA. |
Xiaoze Liu; Junyang Wu; Tianyi Li; Lu Chen; Yunjun Gao; |
| 245 | Hierarchical Self-Attention Embedding for Temporal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, most of them usually treat timestamps as a general feature and cannot take advantage of the potential time series information of the timestamp. To solve these problems, wo propose a new Hierarchical Self-Attention Embedding (HSAE) model which inspired by self-attention mechanism and diachronic embedding technique. |
Xin Ren; Luyi Bai; Qianwen Xiao; Xiangxi Meng; |
| 246 | KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel framework KRACL1 to alleviate the widespread sparsity in KGs with graph context and contrastive learning. |
Zhaoxuan Tan; Zilong Chen; Shangbin Feng; Qingyue Zhang; Qinghua Zheng; Jundong Li; Minnan Luo; |
| 247 | TRAVERS: A Diversity-Based Dynamic Approach to Iterative Relevance Search Over Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we vision a more practical scenario called labeling-based iterative relevance search: instead of effortfully inputting example answer entities, the user effortlessly (e.g., implicitly) labels current answer entities, and is rewarded with improved answer entities in the next iteration. |
Ziyang Li; Yu Gu; Yulin Shen; Wei Hu; Gong Cheng; |
| 248 | IMF: Interactive Multimodal Fusion Model for Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim at better modeling the inter-modality information and thus introduce a novel Interactive Multimodal Fusion (IMF) model to integrate knowledge from different modalities. |
Xinhang Li; Xiangyu Zhao; Jiaxing Xu; Yong Zhang; Chunxiao Xing; |
| 249 | Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel knowledge graph pretraining model KGTransformer that could serve as a uniform KRF module in diverse KG-related tasks. |
Wen Zhang; Yushan Zhu; Mingyang Chen; Yuxia Geng; Yufeng Huang; Yajing Xu; Wenting Song; Huajun Chen; |
| 250 | TEA: Time-aware Entity Alignment in Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a time-aware entity alignment (TEA) model that discovers the entity evolving behaviour by exploring the time contexts in KGs and aggregates various contextual information to make the alignment decision. |
Yu Liu; Wen Hua; Kexuan Xin; Saeid Hosseini; Xiaofang Zhou; |
| 251 | Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we combine the query representations from several models in a unified one to incorporate patterns that are independently captured by each model. |
Cosimo Gregucci; Mojtaba Nayyeri; Daniel Hernández; Steffen Staab; |
| 252 | Knowledge Graph Completion with Counterfactual Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most of them are designed to learn from the observed graph structure, which appears to have imbalanced relation distribution during the training stage. Motivated by the causal relationship among the entities on a knowledge graph, we explore this defect through a counterfactual question: “would the relation still exist if the neighborhood of entities became different from observation?” |
Heng Chang; Jie Cai; Jia Li; |
| 253 | Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We correspondingly propose a mutually-paced knowledge distillation model MP-KD, where a teacher network trained on a source TKG can guide the training of a student network on target TKGs with an alignment module. |
Ruijie Wang; Zheng Li; Jingfeng Yang; Tianyu Cao; Chao Zhang; Bing Yin; Tarek Abdelzaher; |
| 254 | Message Function Search for Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But they are restricted to a single KG form, and the searched models are restricted to a single type of embedding model. To tackle such issues, we propose to build a search space for the message function in graph neural networks (GNNs). |
Shimin Di; Lei Chen; |
| 255 | Cashing in on Contacts: Characterizing The OnlyFans Ecosystem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This results in a wide cross-platform ecosystem geared towards bringing consumers to creators’ accounts. In this paper, we inspect this emerging ecosystem, focusing on content creators and the third-party platforms they connect to. |
Pelayo Vallina; Ignacio Castro; Gareth Tyson; |
| 256 | Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. |
Renhe Jiang; Zhaonan Wang; Yudong Tao; Chuang Yang; Xuan Song; Ryosuke Shibasaki; Shu-Ching Chen; Mei-Ling Shyu; |
| 257 | Automated Content Moderation Increases Adherence to Community Guidelines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, in a large study of Facebook comments (n = 412M), we used a fuzzy regression discontinuity design to measure the impact of automated content moderation on subsequent rule-breaking behavior (number of comments hidden/deleted) and engagement (number of additional comments posted). |
Manoel Horta Ribeiro; Justin Cheng; Robert West; |
| 258 | Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we adopt a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. |
Yunhao Yuan; Koustuv Saha; Barbara Keller; Erkki Tapio Isometsä; Talayeh Aledavood; |
| 259 | Misbehavior and Account Suspension in An Online Financial Communication Platform Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study TradingView, the largest online communication platform for financial trading. |
Taro Tsuchiya; Alejandro Cuevas; Thomas Magelinski; Nicolas Christin; |
| 260 | Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students. |
Bing He; Mustaque Ahamad; Srijan Kumar; |
| 261 | MassNE: Exploring Higher-Order Interactions with Marginal Effect for Massive Battle Outcome Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel Massive battle outcome predictor with margiNal Effect modules, namely MassNE, which comprehensively incorporates individual effects, cooperation effects (i.e., intra-team interactions) and suppression effects (i.e., inter-team interactions) for predicting battle outcomes. |
Yin Gu; Kai Zhang; Qi Liu; Xin Lin; Zhenya Huang; Enhong Chen; |
| 262 | Large-Scale Analysis of New Employee Network Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, by analyzing a large-scale telemetry dataset of more than 10,000 Microsoft employees who joined the company in the first three months of 2022, we describe how new employees interact and telecommute with their colleagues during their “onboarding” period. |
Yulin Yu; Longqi Yang; Siân Lindley; Mengting Wan; |
| 263 | A First Look at Public Service Websites from The Affordability Lens Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents the first large-scale analysis of the affordability of public service websites in developing countries. |
Rumaisa Habib; Aimen Inam; Ayesha Ali; Ihsan Ayyub Qazi; Zafar Ayyub Qazi; |
| 264 | Propaganda Política Pagada: Exploring U.S. Political Facebook Ads En Español Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study differences between political Facebook ads in English and Spanish during 2020, the latest U.S. presidential election. |
Bruno Coelho; Tobias Lauinger; Laura Edelson; Ian Goldstein; Damon McCoy; |
| 265 | Migration Reframed? A Multilingual Analysis on The Stance Shift in Europe During The Ukrainian Crisis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate whether this impression is substantiated by how the topic is reflected in online news and social media, thus linking the representation of the issue on the Web to its perception in society. For this purpose, we combine and adapt leading-edge automatic text processing for a novel multilingual stance detection approach. |
Sergej Wildemann; Claudia Niederée; Erick Elejalde; |
| 266 | Who Funds Misinformation? A Systematic Analysis of The Ad-related Profit Routines of Fake News Sites Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Stifling fake news impact depends on our efforts in limiting the (economic) incentives of fake news producers. In this paper, we aim at enhancing the transparency around these exact incentives, and explore: Who supports the existence of fake news websites via paid ads, either as an advertiser or an ad seller? |
Emmanouil Papadogiannakis; Panagiotis Papadopoulos; Evangelos P. Markatos; Nicolas Kourtellis; |
| 267 | Evidence of Demographic Rather Than Ideological Segregation in News Discussion on Reddit Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We evaluate homophily and heterophily among ideological and demographic groups in a typical opinion formation context: online discussions of current news. |
Corrado Monti; Jacopo D’Ignazi; Michele Starnini; Gianmarco De Francisci Morales; |
| 268 | Online Advertising in Ukraine and Russia During The 2022 Russian Invasion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We hypothesized that online advertising, due to its reach and potential, might have been used to spread information around the 2022 Russian invasion of Ukraine. Thus, to understand the online ad ecosystem during this conflict, we conducted a five-month long large-scale measurement study of online advertising in Ukraine, Russia, and the US. |
Christina Yeung; Umar Iqbal; Yekaterina Tsipenyuk O’Neil; Tadayoshi Kohno; Franziska Roesner; |
| 269 | Understanding The Behaviors of Toxic Accounts on Reddit Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a measurement study of 929K accounts that post toxic comments on Reddit over an 18 month period. |
Deepak Kumar; Jeff Hancock; Kurt Thomas; Zakir Durumeric; |
| 270 | Online Reviews Are Leading Indicators of Changes in K-12 School Attributes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing work on these text reviews has focused on finding words or themes that underlie these perceptions, but has stopped short of using the textual reviews as leading indicators of school performance. In this paper, we investigate to what extent the language used in online reviews of a school is predictive of changes in the attributes of that school, such as its socio-economic makeup and student test scores. |
Linsen Li; Aron Culotta; Douglas N. Harris; Nicholas Mattei; |
| 271 | SeqCare: Sequential Training with External Medical Knowledge Graph for Diagnosis Prediction in Healthcare Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, aiming at by-passing the bias-variance trade-off dilemma, we introduce a new sequential learning framework, dubbed SeqCare, for diagnosis prediction with online medical knowledge graphs. |
Yongxin Xu; Xu Chu; Kai Yang; Zhiyuan Wang; Peinie Zou; Hongxin Ding; Junfeng Zhao; Yasha Wang; Bing Xie; |
| 272 | Longitudinal Assessment of Reference Quality on Wikipedia Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose pairing novice and experienced editors on the same Wikipedia article as a strategy to enhance reference quality. |
Aitolkyn Baigutanova; Jaehyeon Myung; Diego Saez-Trumper; Ai-Jou Chou; Miriam Redi; Changwook Jung; Meeyoung Cha; |
| 273 | Gateway Entities in Problematic Trajectories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show, via a real-world application on Facebook groups, that a simple definition of gateway entities can be leveraged to reduce exposure to problematic content by 1% without any adverse impact on user engagement metrics. Motivated by this finding, we propose several formal definitions of gateways, via both frequentist and survival analysis methods, and evaluate their efficacy in predicting user behavior through offline experiments. |
Xi Leslie Chen; Abhratanu Dutta; Sindhu Ernala; Stratis Ioannidis; Shankar Kalyanaraman; Israel Nir; Udi Weinsberg; |
| 274 | The Thin Ideology of Populist Advertising on Facebook During The 2019 EU Elections Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study compares how populist parties advertised on Facebook during the 2019 European Parliamentary election. |
Arthur Capozzi; Gianmarco De Francisci Morales; Yelena Mejova; Corrado Monti; André Panisson; |
| 275 | Beyond Fine-Tuning: Efficient and Effective Fed-Tuning for Mobile/Web Users Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we attempt to extend the local-user fine-tuning to multi-user fed-tuning with the help of Federated Learning (FL). |
Bingyan Liu; Yifeng Cai; Hongzhe Bi; Ziqi Zhang; Ding Li; Yao Guo; Xiangqun Chen; |
| 276 | Unsupervised Anomaly Detection on Microservice Traces Through Graph VAE Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel dual-variable graph variational autoencoder (VAE) for unsupervised anomaly detection on microservice traces. |
Zhe Xie; Haowen Xu; Wenxiao Chen; Wanxue Li; Huai Jiang; Liangfei Su; Hanzhang Wang; Dan Pei; |
| 277 | Automated WebAssembly Function Purpose Identification With Semantics-Aware Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we construct WASPur, a tool to automatically identify the purposes of WebAssembly functions. |
Alan Romano; Weihang Wang; |
| 278 | FedEdge: Accelerating Edge-Assisted Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds; 2) it does not recognize or utilize model diversity when producing a global model; and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. |
Kaibin Wang; Qiang He; Feifei Chen; Hai Jin; Yun Yang; |
| 279 | CausIL: Causal Graph for Instance Level Microservice Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This poses a challenge for off-the-shelf causal structure detection techniques as they neither incorporate the system architectural domain information nor provide a way to model distributed compute across varying numbers of service instances. To address this, we develop CausIL, which detects a causal structure among service metrics by considering compute distributed across dynamic instances and incorporating domain knowledge derived from system architecture. |
Sarthak Chakraborty; Shaddy Garg; Shubham Agarwal; Ayush Chauhan; Shiv Kumar Saini; |
| 280 | SCTAP: Supporting Scenario-Centric Trigger-Action Programming Based on Software-Defined Physical Environments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an approach called SCTAP which supports Scenario-Centric Trigger-Action Programming based on software-defined physical environments. |
Bingkun Sun; Liwei Shen; Xin Peng; Ziming Wang; |
| 281 | Learning Cooperative Oversubscription for Cloud By Chance-Constrained Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods and industrial practices are over-conservative, ignoring the coordination of diverse resource usage patterns and probabilistic constraints. To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and proposes an effective Chance-Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem. |
Junjie Sheng; Lu Wang; Fangkai Yang; Bo Qiao; Hang Dong; Xiangfeng Wang; Bo Jin; Jun Wang; Si Qin; Saravan Rajmohan; Qingwei Lin; Dongmei Zhang; |
| 282 | CMDiagnostor: An Ambiguity-Aware Root Cause Localization Approach Based on Call Metric Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To the best of our knowledge, we are the first to investigate such ambiguity, which is overlooked in the existing literature. Inspired by the law of large numbers and the Markov properties of network traffic, we propose a regression-based method (named AmSitor) to address this problem effectively. |
Qingyang Yu; Changhua Pei; Bowen Hao; Mingjie Li; Zeyan Li; Shenglin Zhang; Xianglin Lu; Rui Wang; Jiaqi Li; Zhenyu Wu; Dan Pei; |
| 283 | Visual-Aware Testing and Debugging for Web Performance Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents Vetter, a novel and effective system that automatically tests and debugs visual distortions. |
Xinlei Yang; Wei Liu; Hao Lin; Zhenhua Li; Feng Qian; Xianlong Wang; Yunhao Liu; Tianyin Xu; |
| 284 | Demystifying Mobile Extended Reality in Web Browsers: How Far Can We Go? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fill the knowledge gap, in this paper, we conduct an empirical study of mobile XR in web browsers. |
Weichen Bi; Yun Ma; Deyu Tian; Qi Yang; Mingtao Zhang; Xiang Jing; |
| 285 | Look Deep Into The Microservice System Anomaly Through Very Sparse Logs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper solves the problem by proposing MicroCU – a novel approach to diagnose microservice systems using very sparse API logs. |
Xinrui Jiang; Yicheng Pan; Meng Ma; Ping Wang; |
| 286 | FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. |
Kaibin Wang; Qiang He; Feifei Chen; Chunyang Chen; Faliang Huang; Hai Jin; Yun Yang; |
| 287 | DeeProphet: Improving HTTP Adaptive Streaming for Low Latency Live Video By Meticulous Bandwidth Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present DeeProphet, a system for accurate bandwidth prediction in LLLS to improve the performance of HAS. |
Kefan Chen; Bo Wang; Wufan Wang; Xiaoyu Li; Fengyuan Ren; |
| 288 | Is IPFS Ready for Decentralized Video Streaming? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conduct a measurement study with over 28,000 videos hosted on the IPFS network and find that video streaming experiences high stall rates due to relatively high Round Trip Times (RTT). |
Zhengyu Wu; ChengHao Ryan Yang; Santiago Vargas; Aruna Balasubramanian; |
| 289 | SISSI: An Architecture for Semantic Interoperable Self-Sovereign Identity-based Access Control on The Web Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an architecture for authentication and authorization on the Web that is based on the Self-Sovereign Identity paradigm. |
Christoph H.-J. Braun; Vasil Papanchev; Tobias Käfer; |
| 290 | Analyzing The Communication Clusters in Datacenters✱ Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a methodology which revolves around a biclustering approach, allowing us to identify groups of racks and servers which communicate frequently over the network. |
Klaus-Tycho Foerster; Thibault Marette; Stefan Neumann; Claudia Plant; Ylli Sadikaj; Stefan Schmid; Yllka Velaj; |
| 291 | PipeEdge: A Trusted Pipelining Collaborative Edge Training Based on Blockchain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. |
Liang Yuan; Qiang He; Feifei Chen; Ruihan Dou; Hai Jin; Yun Yang; |
| 292 | To Store or Not? Online Data Selection for Federated Learning with Limited Storage Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we take the first step to consider the online data selection for FL with limited on-device storage. |
Chen Gong; Zhenzhe Zheng; Fan Wu; Yunfeng Shao; Bingshuai Li; Guihai Chen; |
| 293 | ELASTIC: Edge Workload Forecasting Based on Collaborative Cloud-Edge Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ELASTIC, which is the first study that leverages a cloud-edge collaborative paradigm for edge workload forecasting with multi-view graphs. |
Yanan Li; Haitao Yuan; Zhe Fu; Xiao Ma; Mengwei Xu; Shangguang Wang; |
| 294 | DDPC: Automated Data-Driven Power-Performance Controller Design On-the-fly for Latency-sensitive Web Services Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present DDPC, a system for autonomic data-driven controller generation for power-latency management. |
Mehmet Savasci; Ahmed Ali-Eldin; Johan Eker; Anders Robertsson; Prashant Shenoy; |
| 295 | DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that the computational cost brought by fine-tuning can be rather unnecessary. |
Zheqi Lv; Wenqiao Zhang; Shengyu Zhang; Kun Kuang; Feng Wang; Yongwei Wang; Zhengyu Chen; Tao Shen; Hongxia Yang; Beng Chin Ooi; Fei Wu; |
| 296 | Detecting Socially Abnormal Highway Driving Behaviors Via Recurrent Graph Attention Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we consider the problem of detecting a variety of socially abnormal driving behaviors, i.e., behaviors that do not conform to the behavior of other nearby drivers. |
Yue Hu; Yuhang Zhang; Yanbing Wang; Daniel Work; |
| 297 | GROUP: An End-to-end Multi-step-ahead Workload Prediction Approach Focusing on Workload Group Behavior Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing approaches mainly analyze the individual changes of each container and do not explicitly model the workload group evolution of containers, resulting in sub-optimal results. Therefore, we propose a workload prediction method, GROUP, which implements the shifts of workload prediction focus from individual to group, workload group behavior representation from data similarity to data correlation, and workload group behavior evolution from implicit modeling to explicit modeling. |
Binbin Feng; Zhijun Ding; |
| 298 | Will Admins Cope? Decentralized Moderation in The Fediverse Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore how large that overhead is, and whether there are solutions to alleviate the burden. |
Ishaku Hassan Anaobi; Aravindh Raman; Ignacio Castro; Haris Bin Zia; Damilola Ibosiola; Gareth Tyson; |
| 299 | BiSR: Bidirectionally Optimized Super-Resolution for Mobile Video Streaming Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we design Bidirectionally Optimized Super-Resolution (BiSR) to improve the quality of experience (QoE) for mobile web users under limited bandwidth. |
Qian Yu; Qing Li; Rui He; Gareth Tyson; Wanxin Shi; Jianhui Lv; Zhenhui Yuan; Peng Zhang; Yulong Lan; Zhicheng Li; |
| 300 | Are Mobile Advertisements in Compliance with App’s Age Group? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first characterize the regulations for mobile ads relating to children. We then propose our novel automated dynamic analysis framework, named AdRambler, that attempts to collect ad content throughout the lifespan of mobile ads and identify their inappropriateness for child app users. |
Yanjie Zhao; Tianming Liu; Haoyu Wang; Yepang Liu; John Grundy; Li Li; |
| 301 | EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. |
Zeqian Dong; Qiang He; Feifei Chen; Hai Jin; Tao Gu; Yun Yang; |
| 302 | HTTP Steady Connections for Robust Web Acceleration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present the notion of HTTP steady connections, which fully utilizes the server’s available network bandwidth during a page load using the promising HTTP/3 server push, transforming the intermittent workload of loading a page into a more steady one. |
Sunjae Kim; Wonjun Lee; |
| 303 | Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate the problem of hashing with Graph Convolutional Network on bipartite graphs for effective Top-N search. |
Yankai Chen; Yixiang Fang; Yifei Zhang; Irwin King; |
| 304 | Beyond Two-Tower: Attribute Guided Representation Learning for Candidate Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel attribute guided representation learning framework (named AGREE) to enhance the candidate retrieval by exploiting query-attribute relevance. |
Hongyu Shan; Qishen Zhang; Zhongyi Liu; Guannan Zhang; Chenliang Li; |
| 305 | Improving Content Retrievability in Search with Controllable Query Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose CtrlQGen, a method that generates queries for a chosen underlying intent—narrow or broad. |
Gustavo Penha; Enrico Palumbo; Maryam Aziz; Alice Wang; Hugues Bouchard; |
| 306 | Learning Denoised and Interpretable Session Representation for Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Meanwhile, the learned latent representation also lacks interpretability that people cannot perceive how the model understands the session. To tackle the above drawbacks, we propose a sparse Lexical-based Conversational REtriever (LeCoRE), which extends the SPLADE model with two well-matched multi-level denoising methods uniformly based on knowledge distillation and external query rewrites to generate denoised and interpretable lexical session representation. |
Kelong Mao; Hongjin Qian; Fengran Mo; Zhicheng Dou; Bang Liu; Xiaohua Cheng; Zhao Cao; |
| 307 | LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, they are prone to overlook local salient phrases and entity mentions in texts, which usually play pivot roles in first-stage retrieval. To mitigate this weakness, we propose to make a dense retriever align a well-performing lexicon-aware representation model. |
Kai Zhang; Chongyang Tao; Tao Shen; Can Xu; Xiubo Geng; Binxing Jiao; Daxin Jiang; |
| 308 | RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a Reinforcement Learning (RL) based Multi-Phase Computation Allocation approach (RL-MPCA), which aims to maximize the total business revenue under the limitation of CRs. |
Jiahong Zhou; Shunhui Mao; Guoliang Yang; Bo Tang; Qianlong Xie; Lebin Lin; Xingxing Wang; Dong Wang; |
| 309 | FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, we propose FINGER, a fast inference method for efficient graph search in AKNNS. |
Patrick Chen; Wei-Cheng Chang; Jyun-Yu Jiang; Hsiang-Fu Yu; Inderjit Dhillon; Cho-Jui Hsieh; |
| 310 | A Passage-Level Reading Behavior Model for Mobile Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct a lab-based user study to investigate users’ fine-grained reading behavior patterns in mobile search. |
Zhijing Wu; Jiaxin Mao; Kedi Xu; Dandan Song; Heyan Huang; |
| 311 | Learning To Rank Resources with GNN Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a graph neural network (GNN) based approach to learning-to-rank that is capable of modeling resource-query and resource-resource relationships. |
Ulugbek Ergashev; Eduard Dragut; Weiyi Meng; |
| 312 | Match4Match: Enhancing Text-Video Retrieval By Maximum Flow with Minimum Cost Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Match4Match, a new text-video retrieval method based on CLIP (Contrastive Language-Image Pretraining) and graph optimization theories. |
Zhongjie Duan; Chengyu Wang; Cen Chen; Wenmeng Zhou; Jun Huang; Weining Qian; |
| 313 | CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present a min-max based Center-guided Adversarial Training, namely CgAT, to improve the robustness of deep hashing networks through worst adversarial examples. |
Xunguang Wang; Yiqun Lin; Xiaomeng Li; |
| 314 | Algorithmic Vibe in Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We define a vibe metric that lets us see the words that a ranker prefers. |
Ali Montazeralghaem; Nick Craswell; Ryen W. White; Ahmed H. Awadallah; Byungki Byun; |
| 315 | Zero-shot Clarifying Question Generation for Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we innovatively explore generating clarifying questions in a zero-shot setting to overcome the cold start problem and we propose a constrained clarifying question generation system which uses both question templates and query facets to guide the effective and precise question generation. |
Zhenduo Wang; Yuancheng Tu; Corby Rosset; Nick Craswell; Ming Wu; Qingyao Ai; |
| 316 | PROD: Progressive Distillation for Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To bridge the gap, we propose PROD, a PROgressive Distillation method, for dense retrieval. |
Zhenghao Lin; Yeyun Gong; Xiao Liu; Hang Zhang; Chen Lin; Anlei Dong; Jian Jiao; Jingwen Lu; Daxin Jiang; Rangan Majumder; Nan Duan; |
| 317 | FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel fast non-autoregressive sequence generation model, namely FANS, to enhance inference efficiency and quality for item list continuation. |
Qijiong Liu; Jieming Zhu; Jiahao Wu; Tiandeng Wu; Zhenhua Dong; Xiao-Ming Wu; |
| 318 | DANCE: Learning A Domain Adaptive Framework for Deep Hashing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, they usually suffer from serious class imbalance in pseudo-labels and suboptimal domain alignment caused by the neglection of the intrinsic structures of two domains. To address this issue, we propose a novel method named unbiaseD duAl hashiNg Contrastive lEarning (DANCE) for domain adaptive image retrieval. |
Haixin Wang; Jinan Sun; Xiang Wei; Shikun Zhang; Chong Chen; Xian-Sheng Hua; Xiao Luo; |
| 319 | Geographic Information Retrieval Using Wikipedia Articles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel approach to automatically determine the latitude-longitude coordinates of appropriate Wikipedia articles with high accuracy, leveraging both text and metadata in the corpus. |
Amir Krause; Sara Cohen; |
| 320 | Everything Evolves in Personalized PageRank Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, in this paper, we aim to solve the Personalized PageRank effectively and efficiently in a fully dynamic setting, i.e., every component in the Personalized PageRank formula is dependent on time. |
Zihao Li; Dongqi Fu; Jingrui He; |
| 321 | Differentiable Optimized Product Quantization and Beyond Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, since data indexing resorts to operator, differentiability is usually achieved by either relaxation or Straight-Through Estimation (STE), which leads to biased gradient and slow convergence. To address these problems, we propose a Differentiable Optimized Product Quantization method (DOPQ) and beyond in this paper. |
Zepu Lu; Defu Lian; Jin Zhang; Zaixi Zhang; Chao Feng; Hao Wang; Enhong Chen; |
| 322 | Incorporating Explicit Subtopics in Personalized Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ExpliPS, a personalized search model that explicitly incorporates query subtopics into personalization. |
Shuting Wang; Zhicheng Dou; Jing Yao; Yujia Zhou; Ji-Rong Wen; |
| 323 | Optimizing Guided Traversal for Fast Learned Sparse Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent studies show that BM25-driven dynamic index skipping can greatly accelerate MaxScore-based document retrieval based on the learned sparse representation derived by DeepImpact. This paper investigates the effectiveness of such a traversal guidance strategy during top k retrieval when using other models such as SPLADE and uniCOIL, and finds that unconstrained BM25-driven skipping could have a visible relevance degradation when the BM25 model is not well aligned with a learned weight model or when retrieval depth k is small. |
Yifan Qiao; Yingrui Yang; Haixin Lin; Tao Yang; |
| 324 | Optimizing Feature Set for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The latter identifies useful feature interaction from all available features, resulting in many redundant features in the feature set. In this paper, we propose a novel method named OptFS to address these problems. |
Fuyuan Lyu; Xing Tang; Dugang Liu; Liang Chen; Xiuqiang He; Xue Liu; |
| 325 | A Reference-Dependent Model for Web Search Evaluation: Understanding and Measuring The Experience of Boundedly Rational Users Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a new evaluation metric framework, namely Reference Dependent Metric (ReDeM), for assessing query-level search by incorporating the effect of reference dependence into the modelling of user search behavior. |
Nuo Chen; Jiqun Liu; Tetsuya Sakai; |
| 326 | Filtered-DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present two algorithms with native support for faster and more accurate filtered ANNS queries: one with streaming support, and another based on batch construction. |
Siddharth Gollapudi; Neel Karia; Varun Sivashankar; Ravishankar Krishnaswamy; Nikit Begwani; Swapnil Raz; Yiyong Lin; Yin Zhang; Neelam Mahapatro; Premkumar Srinivasan; Amit Singh; Harsha Vardhan Simhadri; |
| 327 | Ad Auction Design with Coupon-Dependent Conversion Rate in The Auto-bidding World Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we jointly design the coupon value computation, slot allocation, and payment of online advertising in an auto-bidding world. |
Bonan Ni; Xun Wang; Qi Zhang; Pingzhong Tang; Zhourong Chen; Tianjiu Yin; Liangni Lu; Xiaobing Liu; Kewu Sun; Zhe Ma; |
| 328 | Autobidding Auctions in The Presence of User Costs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To improve the performance of VCG, We propose a new variant of VCG based on properly chosen cost multipliers, and prove that there exist auction-dependent and bidder-dependent cost multipliers that guarantee approximation ratios of 1/2 and 1/4 respectively in terms of the social welfare. |
Yuan Deng; Jieming Mao; Vahab Mirrokni; Hanrui Zhang; Song Zuo; |
| 329 | Multitask Peer Prediction With Task-dependent Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, we propose the joint-disjoint task framework, a new paradigm which builds upon the previous penalty-bonus task framework. |
Yichi Zhang; Grant Schoenebeck; |
| 330 | Stability and Efficiency of Personalised Cultural Markets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This has made formal analysis of these markets improbable. In this paper, we remedy this by establishing robust connections between influence dynamics and optimization processes, in trial-offer markets where the consumer preferences are modelled by multinomial logit. |
Haiqing Zhu; Yun Kuen Cheung; Lexing Xie; |
| 331 | Learning with Exposure Constraints in Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a contextual multi-armed bandit setting to model the dependency of content providers on exposure. |
Omer Ben-Porat; Rotem Torkan; |
| 332 | High-Effort Crowds: Limited Liability Via Tournaments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we suggest using a rank-order payment function (tournament). |
Yichi Zhang; Grant Schoenebeck; |
| 333 | Auctions Without Commitment in The Auto-bidding World Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: From an auction theoretic perspective however, this trend seems to go against foundational results that postulate that for profit-maximizing (aka quasi-linear) bidders, it is optimal to use a classic bidding system like marginal CPA (mCPA) bidding rather than using strategies like tCPA. In this paper we rationalize the adoption of such seemingly sub-optimal bidding within the canonical quasi-linear framework. |
Andres Perlroth; Aranyak Mehta; |
| 334 | Learning to Bid in Contextual First Price Auctions✱ Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate the problem of how to bid in repeated contextual first price auctions for a single learner (the bidder). |
Ashwinkumar Badanidiyuru; Zhe Feng; Guru Guruganesh; |
| 335 | Online Resource Allocation in Markov Chains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider dynamic markets, whose states evolve as a random walk in a market-specific Markov Chain. |
Jianhao Jia; Hao Li; Kai Liu; Ziqi Liu; Jun Zhou; Nikolai Gravin; Zhihao Gavin Tang; |
| 336 | Worst-Case Welfare of Item Pricing in The Tollbooth Problem Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the worst-case welfare of item pricing in the tollbooth problem. |
Zihan Tan; Yifeng Teng; Mingfei Zhao; |
| 337 | Dynamic Interventions for Networked Contagions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We study the problem of designing dynamic intervention policies for minimizing cascading failures in online financial networks, as well we more general demand-supply networks. |
Marios Papachristou; Siddhartha Banerjee; Jon Kleinberg; |
| 338 | Randomized Pricing with Deferred Acceptance for Revenue Maximization with Submodular Objectives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Observing this, the recent studies (e.g., [4, 5, 11]) have considered non-monotone submodular objectives or more complex constraints such as a k-system constraint. In this study, we follow this line of research and propose truthful BFMs with improved performance bounds for non-monotone submodular objectives with or without a k-system constraint. |
He Huang; Kai Han; Shuang Cui; Jing Tang; |
| 339 | Eligibility Mechanisms: Auctions Meet Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we investigate the design of truthful IR mechanisms, which we term eligibility mechanisms. |
Gagan Goel; Renato Paes Leme; Jon Schneider; David Thompson; Hanrui Zhang; |
| 340 | Online Bidding Algorithms for Return-on-Spend Constrained Advertisers✱ Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study online auto-bidding algorithms for a single advertiser maximizing value under the Return-on-Spend (RoS) constraint, quantifying performance in terms of regret relative to the optimal offline solution that knows all queries a priori. |
Zhe Feng; Swati Padmanabhan; Di Wang; |
| 341 | Efficiency of Non-Truthful Auctions in Auto-bidding: The Power of Randomization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The main contribution of this paper is to characterize the efficiency across the spectrum of all auctions, including non-truthful auctions for which optimal bidding may be complex. |
Christopher Liaw; Aranyak Mehta; Andres Perlroth; |
| 342 | Fairness-aware Guaranteed Display Advertising Allocation Under Traffic Cost Constraint Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an optimal allocation model for GD contracts considering optimizing three objectives: maximizing guaranteed delivery and impressions’ quality and minimizing the extra traffic cost of GD contracts to increase overall revenue. |
Liang Dai; Zhonglin Zu; Hao Wu; Liang Wang; Bo Zheng; |
| 343 | Is Your Digital Neighbor A Reliable Investment Advisor? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We discovered that 1) staunchly �bullish� investors on Twitter often took much more moderate, if not outright opposite, positions in their own trades when the market was down, 2) their followers tended to align their positions with bullish Twitter outlooks, and 3) moderate voices on Twitter (and their own followers) were on the other hand far more consistent with their actual investment strategies. |
Daisuke Kawai; Alejandro Cuevas; Bryan Routledge; Kyle Soska; Ariel Zetlin-Jones; Nicolas Christin; |
| 344 | Platform Behavior Under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. |
Xintong Wang; Gary Qiurui Ma; Alon Eden; Clara Li; Alexander Trott; Stephan Zheng; David Parkes; |
| 345 | Near-Optimal Experimental Design Under The Budget Constraint in Online Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a model to describe two-sided platforms where buyers have limited budgets. |
Yongkang Guo; Yuan Yuan; Jinshan Zhang; Yuqing Kong; Zhihua Zhu; Zheng Cai; |
| 346 | Impartial Selection with Prior Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We analyze the performance of a natural selection mechanism that we call approval voting with default (AVD) and show that it achieves a additive guarantee for opinion poll and a for a priori popularity inputs, where n is the number of individuals. We consider this polylogarithmic bound as our main technical contribution. We complement this last result by showing that our analysis is close to tight, showing an Ω(ln n) lower bound. |
Ioannis Caragiannis; George Christodoulou; Nicos Protopapas; |
| 347 | Maximizing Submodular Functions for Recommendation in the Presence of Biases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We study the maximization of a family of submodular functions that capture functions arising in the aforementioned applications. |
Anay Mehrotra; Nisheeth K. Vishnoi; |
| 348 | Do Language Models Plagiarize? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For instance, models can generate paraphrased sentences that are contextually similar to training samples. In this work, therefore, we study three types of plagiarism (i.e., verbatim, paraphrase, and idea) among GPT-2 generated texts, in comparison to its training data, and further analyze the plagiarism patterns of fine-tuned LMs with domain-specific corpora which are extensively used in practice. |
Jooyoung Lee; Thai Le; Jinghui Chen; Dongwon Lee; |
| 349 | Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We organized these metrics into a ‘decision-tree style’ support framework designed to help practitioners scope fairness objectives and identify fairness metrics relevant to their recommendation domain and application context. To explore the feasibility of this approach, we conducted 15 semi-structured interviews using this framework to assess which challenges practitioners may face when scoping fairness objectives and metrics for their system, and which further support may be needed beyond such tools. |
Jessie J. Smith; Lex Beattie; Henriette Cramer; |
| 350 | Simplistic Collection and Labeling Practices Limit The Utility of Benchmark Datasets for Twitter Bot Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These tools employ machine learning and often achieve near-perfect performance for classification on existing datasets, suggesting bot detection is accurate, reliable and fit for use in downstream applications. We provide evidence that this is not the case and show that high performance is attributable to limitations in dataset collection and labeling rather than sophistication of the tools. |
Chris Hays; Zachary Schutzman; Manish Raghavan; Erin Walk; Philipp Zimmer; |
| 351 | A Method to Assess and Explain Disparate Impact in Online Retailing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a method for assessing whether algorithmic decision making induces disparate impact in online retailing. |
Rafael Becerril-Arreola; |
| 352 | Path-specific Causal Fair Prediction Via Auxiliary Graph Structure Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods based on path-specific causal fairness either require graph structure as the prior knowledge or have high complexity in the calculation of path-specific effect. To tackle these challenges, we propose a novel casual graph based fair prediction framework which integrates graph structure learning into fair prediction to ensure that unfair pathways are excluded in the causal graph. |
Liuyi Yao; Yaliang Li; Bolin Ding; Jingren Zhou; Jinduo Liu; Mengdi Huai; Jing Gao; |
| 353 | Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, traditional MTL requires labels for all tasks to be present for every data point. To address this, we propose Conditional MTL (CondMTL), wherein only training examples relevant to the given demographic group are considered by the loss function. |
Soumyajit Gupta; Sooyong Lee; Maria De-Arteaga; Matthew Lease; |
| 354 | P-MMF: Provider Max-min Fairness Re-ranking in Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder recommender systems. |
Chen Xu; Sirui Chen; Jun Xu; Weiran Shen; Xiao Zhang; Gang Wang; Zhenhua Dong; |
| 355 | Towards Explainable Collaborative Filtering with Taste Clusters Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Adding explainability to recommendation models can not only increase trust in the decision-making process, but also have multiple benefits such as providing persuasive explanations for item recommendations, creating explicit profiles for users and items, and assisting item producers in design improvements. In this paper, we propose a neat and effective Explainable Collaborative Filtering (ECF) model that leverages interpretable cluster learning to achieve the two most demanding objectives: (1) Precise – the model should not compromise accuracy in the pursuit of explainability; and (2) Self-explainable – the model’s explanations should truly reflect its decision-making process, not generated from post-hoc methods. |
Yuntao Du; Jianxun Lian; Jing Yao; Xiting Wang; Mingqi Wu; Lu Chen; Yunjun Gao; Xing Xie; |
| 356 | Fairly Adaptive Negative Sampling for Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Fairly adaptive Negative sampling approach (FairNeg), which improves item group fairness via adaptively adjusting the group-level negative sampling distribution in the training process. |
Xiao Chen; Wenqi Fan; Jingfan Chen; Haochen Liu; Zitao Liu; Zhaoxiang Zhang; Qing Li; |
| 357 | HateProof: Are Hateful Meme Detection Systems Really Robust? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the availability of implicit content payloads makes them fairly challenging to be detected by existing hateful meme detection systems. In this paper, we present a use case study to analyze such systems’ vulnerabilities against external adversarial attacks. |
Piush Aggarwal; Pranit Chawla; Mithun Das; Punyajoy Saha; Binny Mathew; Torsten Zesch; Animesh Mukherjee; |
| 358 | Towards Fair Allocation in Social Commerce Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The enormous product space in such platforms prohibits manual search, and motivates the need for recommendation algorithms to effectively allocate product exposure and, consequently, earning opportunities. In this work, we focus on the fairness of such allocations in social commerce platforms and formulate the problem of assigning products to re-sellers as a fair division problem with indivisible items under two-sided cardinality constraints, wherein each product must be given to at least a certain number of re-sellers and each re-seller must get a certain number of products. |
Anjali Gupta; Shreyans J. Nagori; Abhijnan Chakraborty; Rohit Vaish; Sayan Ranu; Prajit Prashant Nadkarni; Narendra Varma Dasararaju; Muthusamy Chelliah; |
| 359 | Fairness-Aware Clique-Preserving Spectral Clustering of Temporal Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, in practice, when the graph (e.g., social networks) topology constantly changes over time, one natural question is how can we ensure the compactness and demographic parity at each timestamp efficiently. To address these problems, we start from the static setting and propose a spectral method that preserves clique connections and incorporates demographic fairness constraints in returned clusters at the same time. |
Dongqi Fu; Dawei Zhou; Ross Maciejewski; Arie Croitoru; Marcus Boyd; Jingrui He; |
| 360 | DualFair: Fair Representation Learning at Both Group and Individual Levels Via Contrastive Self-supervision Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. |
Sungwon Han; Seungeon Lee; Fangzhao Wu; Sundong Kim; Chuhan Wu; Xiting Wang; Xing Xie; Meeyoung Cha; |
| 361 | Fairness in Model-sharing Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we consider two notions of fairness that each may be appropriate in different circumstances: egalitarian fairness (which aims to bound how dissimilar error rates can be) and proportional fairness (which aims to reward players for contributing more data). |
Kate Donahue; Jon Kleinberg; |
| 362 | PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Given existing GNN explanation methods only address node/graph-level tasks, we propose Path-based GNN Explanation for heterogeneous Link prediction (PaGE-Link) that generates explanations with connection interpretability, enjoys model scalability, and handles graph heterogeneity. |
Shichang Zhang; Jiani Zhang; Xiang Song; Soji Adeshina; Da Zheng; Christos Faloutsos; Yizhou Sun; |
| 363 | Combining Worker Factors For Heterogeneous Crowd Task Assignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study is an initial step towards understanding whether and how multiple parameters such as cognitive skills, mood, personality, alertness, comprehension skill, and social and physical context of workers can be leveraged in tandem to improve worker performance estimations in heterogeneous micro tasks. |
Senuri Wijenayake; Danula Hettiachchi; Jorge Goncalves; |
| 364 | Hidden Indicators of Collective Intelligence in Crowdfunding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we explore whether crowds furnish collective intelligence benefits in crowdfunding systems. |
Emőke-Ágnes Horvát; Henry Kudzanai Dambanemuya; Jayaram Uparna; Brian Uzzi; |
| 365 | A Dataset on Malicious Paper Bidding in Peer Review Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A critical impediment towards creating and evaluating methods to mitigate this issue is the lack of any publicly-available data on malicious paper bidding. In this work, we collect and publicly release a novel dataset to fill this gap, collected from a mock conference activity where participants were instructed to bid either honestly or maliciously. |
Steven Jecmen; Minji Yoon; Vincent Conitzer; Nihar B. Shah; Fei Fang; |
| 366 | Multiview Representation Learning from Crowdsourced Triplet Comparisons Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose an end-to-end inductive deep learning framework to solve the multiview representation learning problem. |
Xiaotian Lu; Jiyi Li; Koh Takeuchi; Hisashi Kashima; |
| 367 | HybridEval: A Human-AI Collaborative Approach for Evaluating Design Ideas at Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate workers’ rating behavior and compare it with experts. |
Sepideh Mesbah; Ines Arous; Jie Yang; Alessandro Bozzon; |
| 368 | Sedition Hunters: A Quantitative Study of The Crowdsourced Investigation Into The 2021 U.S. Capitol Attack Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through a case study of Sedition Hunters, a Twitter community whose goal is to identify individuals who participated in the 2021 U.S. Capitol Attack, we explore what are the main topics or targets of the community, who participates in the community, and how. |
Tianjiao Yu; Sukrit Venkatagiri; Ismini Lourentzou; Kurt Luther; |
| 369 | Human-in-the-loop Regular Expression Extraction for Single Column Format Inconsistency Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel hybrid human-machine system, namely “Data-Scanner-4C”, which leverages crowdsourcing to address syntactic format inconsistencies in a single column effectively. |
Shaochen Yu; Lei Han; Marta Indulska; Shazia Sadiq; Gianluca Demartini; |
| 370 | Identifying Creative Harmful Memes Via Prompt Based Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Further, using conventional pretrained encoders for text and images exhibits a greater semantic gap in feature spaces and leads to low performance. To address these gaps, this paper reformulates a harmful meme analysis as an auto-filling and presents a prompt-based approach to identify harmful memes. |
Junhui Ji; Wei Ren; Usman Naseem; |
| 371 | The Harmonic Memory: A Knowledge Graph of Harmonic Patterns As A Trustworthy Framework for Computational Creativity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enable trustworthy machine creativity, we introduce the Harmonic Memory, a Knowledge Graph (KG) of harmonic patterns extracted from a large and heterogeneous musical corpus. |
Jacopo de Berardinis; Albert Meroño-Peñuela; Andrea Poltronieri; Valentina Presutti; |
| 372 | SA-Fusion: Multimodal Fusion Approach for Web-based Human-Computer Interaction in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The previous hand detection and 3D hand pose estimation methods are usually based on single modality such as RGB or depth data, which are not available in some scenarios in unconstrained environments due to the differences between the two modalities. To address this problem, we propose a multimodal fusion approach, named Scene-Adapt Fusion (SA-Fusion), which can fully utilize the complementarity of RGB and depth modalities in web-based HCI tasks. |
Xingyu Liu; Pengfei Ren; Yuchen Chen; Cong Liu; Jing Wang; Haifeng Sun; Qi Qi; Jingyu Wang; |
| 373 | A Prompt Log Analysis of Text-to-Image Generation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conduct the first comprehensive analysis of large-scale prompt logs collected from multiple text-to-image generation systems. |
Yutong Xie; Zhaoying Pan; Jinge Ma; Luo Jie; Qiaozhu Mei; |
| 374 | CAM: A Large Language Model-based Creative Analogy Mining Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose both unsupervised and supervised instantiations of the framework so that it can be used even without any annotated data. |
Bhavya Bhavya; Jinjun Xiong; Chengxiang Zhai; |
| 375 | Tangible Web: An Interactive Immersion Virtual Reality Creativity System That Travels Across Reality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a case study of a creative approach taken by a tourist attraction venue in developing a physical network system that allows visitors to enhance VR’s aesthetic aspects based on environmental parameters gathered by external sensors. |
Simin Yang; Ze Gao; Reza Hadi Mogavi; Pan Hui; Tristan Braud; |
| 376 | Coherent Topic Modeling for Creative Multimodal Data on Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite this potential, many existing multimodal topic models do not take these criteria into account, resulting in poor quality topics being generated. Therefore, we proposed a Coherent Topic modeling for Multimodal Data (CTM-MM), which takes into account that text in social media posts typically relates to one topic, while images can contain information about multiple topics. |
Junaid Rashid; Jungeun Kim; Usman Naseem; |
| 377 | Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To advance the HMC research and benefit more diverse populations, we present the Nairaland health mention dataset (NHMD), a new dataset collected from a dedicated web forum for Nigerians. |
Olanrewaju Tahir Aduragba; Jialin Yu; Alexandra Cristea; Yang Long; |
| 378 | Facility Relocation Search For Good: When Facility Exposure Meets User Convenience Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel facility relocation problem where facilities (and their services) are portable, which is a combinatorial search problem with many practical applications. |
Hui Luo; Zhifeng Bao; J. Shane Culpepper; Mingzhao Li; Yanchang Zhao; |
| 379 | A Multi-task Model for Emotion and Offensive Aided Stance Detection of Climate Change Tweets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address the United Nations Sustainable Development Goal 13: Climate Action by focusing on identifying public attitudes toward climate change on social media platforms such as Twitter. |
Apoorva Upadhyaya; Marco Fisichella; Wolfgang Nejdl; |
| 380 | Learning Faithful Attention for Interpretable Classification of Crisis-Related Microblogs Under Constrained Human Budget Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a two-stage approach to learn the rationales under minimal human supervision and derive faithful machine attention. |
Thi Huyen Nguyen; Koustav Rudra; |
| 381 | Exploring Social Media for Early Detection of Depression in COVID-19 Patients Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. |
Jiageng Wu; Xian Wu; Yining Hua; Shixu Lin; Yefeng Zheng; Jie Yang; |
| 382 | Attacking Fake News Detectors Via Manipulating News Social Engagement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the first adversarial attack framework against Graph Neural Network (GNN)-based fake news detectors to probe their robustness. |
Haoran Wang; Yingtong Dou; Canyu Chen; Lichao Sun; Philip S. Yu; Kai Shu; |
| 383 | Cross-center Early Sepsis Recognition By Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Because electronic medical records (EMRs) contain sensitive personal information, privacy protection is unavoidable and essential for multi-hospital collaboration. In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve early recognition of this disease. |
Ruiqing Ding; Fangjie Rong; Xiao Han; Leye Wang; |
| 384 | ContrastFaux: Sparse Semi-supervised Fauxtography Detection on The Web Using Multi-view Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study a critical type of online misinformation, namely fauxtography, where the image and associated text of a social media post jointly convey a questionable or false sense. |
Ruohan Zong; Yang Zhang; Lanyu Shang; Dong Wang; |
| 385 | CaML: Carbon Footprinting of Household Products with Zero-Shot Semantic Text Similarity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present CaML, an algorithm to automate EIO-LCA using semantic text similarity matching by leveraging the text descriptions of the product and the industry sector. |
Bharathan Balaji; Venkata Sai Gargeya Vunnava; Geoffrey Guest; Jared Kramer; |
| 386 | Identifying Checkworthy CURE Claims on Twitter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing studies on claim detection do not specifically focus on medical cure aspects, neither do they address if a cure claim is “checkworthy", an indicator of whether a claim is potentially beneficial or harmful, if unchecked. In this paper, we address these limitations by compiling CW-CURE, a novel dataset of CURE tweets, namely tweets containing claims on prevention, diagnoses, risks, treatments, and cures of medical conditions. |
Sujatha Das Gollapalli; Mingzhe Du; See-Kiong Ng; |
| 387 | MSQ-BioBERT: Ambiguity Resolution to Enhance BioBERT Medical Question-Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduced a novel approach called the Multiple Synonymous Questions BioBERT (MSQ-BioBERT), which integrates question augmentation, rather than the typical single question used by traditional BioBERT, to elevate BioBERT’s performance on medical QA tasks. |
Muzhe Guo; Muhao Guo; Edward T. Dougherty; Fang Jin; |
| 388 | Interpreting Wealth Distribution Via Poverty Map Inference Using Multimodal Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we propose a pipeline of machine learning models to infer the mean and standard deviation of wealth across multiple geographically clustered populated places, and illustrate their performance in Sierra Leone and Uganda. |
Lisette Espín-Noboa; János Kertész; Márton Karsai; |
| 389 | Breaking Filter Bubble: A Reinforcement Learning Framework of Controllable Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we first conduct data analysis from a graph view to observe that the users� feedback is restricted to limited items, verifying the phenomenon of centralized recommendation. We further develop a general simulation framework to derive the procedure of the recommender system, including data collection, model learning, and item exposure, which forms a loop. |
Zhenyang Li; Yancheng Dong; Chen Gao; Yizhou Zhao; Dong Li; Jianye Hao; Kai Zhang; Yong Li; Zhi Wang; |
| 390 | CollabEquality: A Crowd-AI Collaborative Learning Framework to Address Class-wise Inequality in Web-based Disaster Response Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, two critical challenges exist: 1) it is difficult to identify the imbalanced AI results without knowing the ground-truth WebDR labels a priori; ii) it is non-trivial to address the class-wise inequality problem using potentially imperfect crowd labels. To address the above challenges, we develop CollabEquality, an inequality-aware crowd-AI collaborative learning framework that carefully models the inequality bias of both AI and human intelligence from crowdsourcing systems into a principled learning framework. |
Yang Zhang; Lanyu Shang; Ruohan Zong; Huimin Zeng; Zhenrui Yue; Dong Wang; |
| 391 | The Impact of Covid-19 on Online Discussions: The Case Study of The Sanctioned Suicide Forum Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate the impact of COVID-19 on the discussions in the forum. The data show that covid, while being present in the discussions, especially during the first lockdown, has not been the main reason why new users registered to the forum. |
Elisa Sartori; Luca Pajola; Giovanni Da San Martino; Mauro Conti; |
| 392 | EDNet: Attention-Based Multimodal Representation for Classification of Twitter Users Related to Eating Disorders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It achieves an accuracy of up to 94.32% and F1 score of up to 93.91% F1 score. To the best of our knowledge, this is the first such study to propose a multimodal approach for user-level classification according to their engagement with ED content on social media. |
Mohammad Abuhassan; Tarique Anwar; Chengfei Liu; Hannah K Jarman; Matthew Fuller-Tyszkiewicz; |
| 393 | MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Among existing learning-based approaches, the association between drug substructures (i.e., a sub-graph of the molecule that contributes to certain chemical effect) and the target disease is largely overlooked, though the function of drugs in fact exhibits strong relevance with particular substructures. To address this issue, we propose a molecular substructure-aware encoding method entitled MoleRec that entails a hierarchical architecture aimed at modeling inter-substructure interactions and individual substructures’ impact on patient’s health condition, in order to identify those substructures that really contribute to healing patients. |
Nianzu Yang; Kaipeng Zeng; Qitian Wu; Junchi Yan; |
| 394 | Detecting and Limiting Negative User Experiences in Social Media Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an approach that aims at improving item ranking long-term impact. |
Lluis Garcia-Pueyo; Vinodh Kumar Sunkara; Prathyusha Senthil Kumar; Mohit Diwan; Qian Ge; Behrang Javaherian; Vasilis Verroios; |
| 395 | Learning Like Human Annotators: Cyberbullying Detection in Lengthy Social Media Sessions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This is especially true when one aims to use state-of-the-art Transformer-based pre-trained language models, which only take inputs of a limited length. In this paper, we address this limitation of transformer models by proposing a conceptually intuitive framework called LS-CB, which enables cyberbullying detection from lengthy social media sessions. |
Peiling Yi; Arkaitz Zubiaga; |
| 396 | On Detecting Policy-Related Political Ads: An Exploratory Analysis of Meta Ads in 2022 French Election Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on political ads that are related to policy. |
Vera Sosnovik; Romaissa Kessi; Maximin Coavoux; Oana Goga; |
| 397 | Graph-based Village Level Poverty Identification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we make the first attempt to interpret and identify village-level poverty from a graph perspective. |
Jing Ma; Liangwei Yang; Qiong Feng; Weizhi Zhang; Philip S. Yu; |
| 398 | Mapping Flood Exposure, Damage, and Population Needs Using Remote and Social Sensing: A Case Study of 2022 Pakistan Floods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While disaster management efforts were taken, crisis responders struggled to understand the country-wide flood extent, population exposure, urgent needs of affected people, and various types of damage. To tackle this challenge, we leverage remote and social sensing with geospatial data using state-of-the-art machine learning techniques for text and image processing. |
Zainab Akhtar; Umair Qazi; Rizwan Sadiq; Aya El-Sakka; Muhammad Sajjad; Ferda Ofli; Muhammad Imran; |
| 399 | Web Information Extraction for Social Good: Food Pantry Answering As An Example Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we built a food pantry answering framework by combining location-aware information retrieval, web information extraction and domain-specific answering. |
Huan-Yuan Chen; Hong Yu; |
| 400 | Moral Narratives Around The Vaccination Debate on Facebook Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we analysed more than 500,000 public posts and comments from Facebook Pages dedicated to the topic of vaccination to study the role of moral values and, in particular, the understudied role of the Liberty moral foundation from the actual user-generated text. |
Mariano Gastón Beiró; Jacopo D’Ignazi; Victoria Perez Bustos; María Florencia Prado; Kyriaki Kalimeri; |
| 401 | Gender Pay Gap in Sports on A Fan-Request Celebrity Video Site Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The internet is often thought of as a democratizer, enabling equality in aspects such as pay, as well as a tool introducing novel communication and monetization opportunities. In this study we examine athletes on Cameo, a website that enables bi-directional fan-celebrity interactions, questioning whether the well-documented gender pay gaps in sports persist in this digital setting. |
Nazanin Sabri; Stephen Reysen; Ingmar Weber; |
| 402 | Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Especially, recent studies turn to self-supervised contrastive learning with manually designed similarity metrics for urban imagery representation learning and further socioeconomic prediction, which however suffers from effectiveness and robustness issues. To address such issues, in this paper, we propose a Knowledge-infused Contrastive Learning (KnowCL) model for urban imagery-based socioeconomic prediction. |
Yu Liu; Xin Zhang; Jingtao Ding; Yanxin Xi; Yong Li; |
| 403 | Leveraging Existing Literature on The Web and Deep Neural Models to Build A Knowledge Graph Focused on Water Quality and Health Risks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A knowledge graph focusing on water quality in relation to health risks posed by water activities (such as diving or swimming) is not currently available. To address this limitation, we first use existing resources to construct a knowledge graph relevant to water quality and health risks using KNowledge Acquisition and Representation Methodology (KNARM). Subsequently, we explore knowledge graph completion approaches for maintaining and updating the graph. |
Nikita Gautam; David Shumway; Megan Kowalcyk; Sarthak Khanal; Doina Caragea; Cornelia Caragea; Hande Mcginty; Samuel Dorevitch; |
| 404 | Believability and Harmfulness Shape The Virality of Misleading Social Media Posts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study how the perceived believability and harmfulness of misleading posts are associated with their virality on social media. |
Chiara Patricia Drolsbach; Nicolas Pröllochs; |
| 405 | Enhancing Deep Knowledge Tracing with Auxiliary Tasks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we proposed AT-DKT to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i.e., question tagging (QT) prediction task and individualized prior knowledge (IK) prediction task. |
Zitao Liu; Qiongqiong Liu; Jiahao Chen; Shuyan Huang; Boyu Gao; Weiqi Luo; Jian Weng; |
| 406 | Vertical Federated Knowledge Transfer Via Representation Distillation for Healthcare Collaboration Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component. |
Chung-ju Huang; Leye Wang; Xiao Han; |
| 407 | Learning to Simulate Crowd Trajectories with Graph Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose to model the interactions among people and the environment using a heterogeneous graph. |
Hongzhi Shi; Quanming Yao; Yong Li; |