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Paper Digest: ICDM 2018 Highlights

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IEEE International Conference on Data Mining (ICDM) is a premier research conference in data mining. In 2018, it is to be held in Singapore. There were more than 900 paper submissions, of which 190 were accepted.

To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.

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Paper Digest Team
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TABLE 1: ICDM 2018 Papers

Title Authors Highlight
1 On Multi-query Local Community Detection Y. Bian, Y. Yan, W. Cheng, W. Wang, D. Luo and X. Zhang To address this limitation of the existing methods, we propose a novel memory-based random walk method, MRW, that can simultaneously identify multiple target local communities to which the query nodes belong.
2 Realization of Random Forest for Real-Time Evaluation through Tree Framing S. Buschjager, K. Chen, J. Chen and K. Morik In this paper, we introduce a method that optimizes the execution of Decision Trees (DT).
3 Social Recommendation with Missing Not at Random Data J. Chen, C. Wang, M. Ester, Q. Shi, Y. Feng and C. Chen With the explosive growth of online social networks, many social recommendation methods have been proposed and demonstrated that social information has potential to improve the recommendation performance.
4 Prerequisite-Driven Deep Knowledge Tracing P. Chen, Y. Lu, V. W. Zheng and Y. Pian In order to address this issue, we advocate for and propose to incorporate the knowledge structure information, especially the prerequisite relations between pedagogical concepts, into the knowledge tracing model.
5 TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction T. Chen et al. Hence, we gain insights from the encoder-decoder recurrent neural network (RNN) structure, and propose a novel framework named TADA to carry out trend alignment with dualattention, multi-task RNNs for sales prediction.
6 Rational Neural Networks for Approximating Graph Convolution Operator on Jump Discontinuities Z. Chen, F. Chen, R. Lai, X. Zhang and C. Lu In this paper, the superiority of rational approximation is exploited for graph signal recovering.
7 Learning Community Structure with Variational Autoencoder J. J. Choong, X. Liu and T. Murata This paper proposes a deep generative model for community detection and network generation.
8 Imbalanced Augmented Class Learning with Unlabeled Data by Label Confidence Propagation S. Ding, X. Liu and M. Zhang We propose a novel approach Label Confidence Propagation (LCP) to tackle the problem of imbalanced augmented class learning with unlabeled data.
9 Sequential Pattern Sampling with Norm Constraints L. Diop, C. T. Diop, A. Giacometti, D. Li and A. Soulet In this paper, we propose the first method for sequential pattern sampling.
10 Probabilistic Streaming Tensor Decomposition Y. Du, Y. Zheng, K. Lee and S. Zhe To address this issue, we propose POST, a PrObabilistic Streaming Tensor decomposition algorithm, which enables real-time updates and predictions upon receiving new tensor entries, and supports dynamic growth of all the modes.
11 Hierarchical Hybrid Feature Model for Top-N Context-Aware Recommendation Y. Du, H. Liu, Z. Wu and X. Zhang In this paper, we propose a succinct hierarchical framework named Hierarchical Hybrid Feature Model (HHFM).
12 Deep Semantic Correlation Learning Based Hashing for Multimedia Cross-Modal Retrieval X. Gong, L. Huang and F. Wang The major contribution in this work is to effectively automatically construct the semantic correlation between data representation and demonstrate how to utilize correlation information to generate hash codes for new samples.
13 Learning Sequential Behavior Representations for Fraud Detection J. Guo, G. Liu, Y. Zuo and J. Wu Therefore, in this paper, we model the attributed behavioral sequences generated from consecutive behaviors, in order to capture the sequential patterns, while those deviate from the pattern can be regarded as fraudulence.
14 Defending Against Adversarial Samples Without Security through Obscurity W. Guo et al. In this work, we investigate by examining how previous research dealt with this and propose a generic approach to enhance a DNN’s resistance to adversarial samples.
15 EDLT: Enabling Deep Learning for Generic Data Classification H. Han, X. Zhu and Y. Li This paper proposes to enable deep learning for generic machine learning tasks.
16 dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction H. Huang, B. Cao, P. S. Yu, C. Wang and A. D. Leow In this study, we use a custom keyboard which collects keystrokes’ meta-data and accelerometer values.
17 Asynchronous Dual Free Stochastic Dual Coordinate Ascent for Distributed Data Mining Z. Huo, X. Jiang and H. Huang In this paper, we address these two issues by proposing novel distributed asynchronous dual free stochastic dual coordinate ascent algorithm for distributed data mining.
18 Representing Networks with 3D Shapes S. Jin and R. Zafarani We present a linear time algorithm to build Kronecker hulls.
19 Cross-Domain Labeled LDA for Cross-Domain Text Classification B. Jing, C. Lu, D. Wang, F. Zhuang and C. Niu To address this problem, we propose a novel group alignment which aligns the semantics at group level.
20 Self-Attentive Sequential Recommendation W. Kang and J. McAuley The goal of our work is to balance these two goals, by proposing a self-attention based sequential model (SASRec) that allows us to capture long-term semantics (like an RNN), but, using an attention mechanism, makes its predictions based on relatively few actions (like an MC).
21 Explainable Time Series Tweaking via Irreversible and Reversible Temporal Transformations I. Karlsson, J. Rebane, P. Papapetrou and A. Gionis In this paper, we formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier that provides a particular classification decision for the time series, we want to find the minimum number of changes to be performed to the given time series so that the classifier changes its decision to another class.
22 Utilizing In-store Sensors for Revisit Prediction S. Kim and J. Lee Using Wi-Fi fingerprinting data from ZOYI, we propose a systematic framework to predict the revisit intention of customers using only signals received from their mobile devices.
23 Fast Single-Class Classification and the Principle of Logit Separation G. Keren, S. Sabato and B. Schuller We propose a natural principle, the Principle of Logit Separation, as a guideline for choosing and designing losses suitable for the SLC.
24 Summarizing Network Processes with Network-Constrained Boolean Matrix Factorization F. Kocayusufoglu, M. X. Hoang and A. K. Singh In this work, our goal is to summarize different processes in a network by a small yet interpretable set of network patterns, each of which represents a local community of connected nodes frequently participating in the same network processes.
25 SSDMV: Semi-Supervised Deep Social Spammer Detection by Multi-view Data Fusion C. Li, S. Wang, L. He, P. S. Yu, Y. Liang and Z. Li In this paper, we propose a Semi-Supervised Deep social spammer detection model by Multi-View data fusion (SSDMV).
26 Accelerating Experimental Design by Incorporating Experimenter Hunches C. Li et al. In this paper, we consider per-variable monotonic trend in the underlying property that results in a unimodal trend in those variables for a target value optimization.
27 Concept Mining via Embedding K. Li, H. Zha, Y. Su and X. Yan In this work, we study the problem of concept mining, which serves as the first step in transforming unstructured text into structured information, and supports downstream analytical tasks such as information extraction, organization, recommendation and search.
28 A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games X. Liu et al. In this paper, we present the first large-scale churn prediction solution for mobile games. To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions.
29 Enhancing Very Fast Decision Trees with Local Split-Time Predictions V. Losing, H. Wersing and B. Hammer In this paper, we increase the efficiency even further by replacing its global splitting scheme, which periodically tries to split every nmin examples.
30 Collective Human Behavior in Cascading System: Discovery, Modeling and Applications Y. Lu et al. In this paper, we examine a real-world online social media with more than 1.7 million information spreading records, which explicitly document the detailed human behavior in this online information cascading system.
31 ResumeNet: A Learning-Based Framework for Automatic Resume Quality Assessment Y. Luo, H. Zhang, Y. Wang, Y. Wen and X. Zhang To deal with the label deficiency issue in the dataset, we propose several variants of the model by either utilizing the pair/triplet-based loss, or introducing some semi-supervised learning technique to make use of the abundant unlabeled data. Since there is also no public dataset for model training and evaluation, we build a dataset for RQA by collecting around 10K resumes, which are provided by a private resume management company.
32 Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms P. Mandros, M. Boley and J. Vreeken In this paper, we systematically explore the algorithmic implications of using this measure for optimization.
33 Tell me Something My Friends do not Know: Diversity Maximization in Social Networks A. Matakos and A. Gionis In this paper we propose a novel approach to address the problem of breaking filter bubbles in social media.
34 Intelligent Salary Benchmarking for Talent Recruitment: A Holistic Matrix Factorization Approach Q. Meng, H. Zhu, K. Xiao and H. Xiong To this end, in this paper, we propose a data-driven approach for intelligent salary benchmarking based on large-scale fine-grained online recruitment data.
35 Maximally Consistent Sampling and the Jaccard Index of Probability Distributions R. Moulton and Y. Jiang We introduce simple, efficient algorithms for computing a MinHash of a probability distribution, suitable for both sparse and dense data, with equivalent running times to the state of the art for both cases.
36 Apk2vec: Semi-Supervised Multi-view Representation Learning for Profiling Android Applications A. Narayanan, C. Soh, L. Chen, Y. Liu and L. Wang Towards this goal, we design a semisupervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app.
37 Collaborative Translational Metric Learning C. Park, D. Kim, X. Xie and H. Yu In this paper, we propose TransCF to discover such latent user-item relationships embodied in implicit user-item interactions.
38 Privacy-Preserving Temporal Record Linkage T. Ranbaduge and P. Christen We propose a novel protocol based on Bloom filter encoding which incorporates the temporal information available in records during the linkage process. Based on these probabilities we generate a set of masking Bloom filters to adjust the similarities between record pairs.
39 SuperPart: Supervised Graph Partitioning for Record Linkage R. Reas, S. Ash, R. Barton and A. Borthwick We present SuperPart, a scalable, supervised learning approach to graph partitioning. Finally, to bolster additional research in this domain, we release three new datasets derived from real-world Amazon product data along with ground-truth partitionings.
40 Finding Events in Temporal Networks: Segmentation Meets Densest-Subgraph Discovery P. Rozenshtein, F. Bonchi, A. Gionis, M. Sozio and N. Tatti In this paper we study the problem of discovering a timeline of events in a temporal network.
41 DipTransformation: Enhancing the Structure of a Dataset and Thereby Improving Clustering B. Schelling and C. Plant The aim of this work is to present a technique which enhances the data set by re-scaling and transforming its features and thus emphasizing and accentuating its structure.
42 ProSecCo: Progressive Sequence Mining with Convergence Guarantees S. Servan-Schreiber, M. Riondato and E. Zgraggen We present PROSECCO, an algorithm for the progressive mining of frequent sequences from large transactional datasets: it processes the dataset in blocks and outputs, after having analyzed each block, a high-quality approximation of the collection of frequent sequences.
43 Local Low-Rank Hawkes Processes for Temporal User-Item Interactions J. Shang and M. Sun To tackle this challenge, we propose local low-rank Hawkes processes to model large-scale user-item interactions, which efficiently captures the correlations of Hawkes processes in different dimensions.
44 Multi-label Learning with Label Enhancement R. Shao, N. Xu and X. Geng In this paper, we propose a novel multi-label learning framework called LEMLL, i.e., Label Enhanced Multi-Label Learning, which incorporates regression of the numerical labels and label enhancement into a unified framework.
45 Synthetic Oversampling with the Majority Class: A New Perspective on Handling Extreme Imbalance S. Sharma, C. Bellinger, B. Krawczyk, O. Zaiane and N. Japkowicz We propose a novel method for synthetic oversampling that uses the rich information inherent in the majority class to synthesize minority class data.
46 GINA: Group Gender Identification Using Privacy-Sensitive Audio Data J. Shen, O. Lederman, J. Cao, F. Berg, S. Tang and A. Pentland In this paper, we make the first attempt to identify group gender using privacy-sensitive audio.
47 Deep Headline Generation for Clickbait Detection K. Shu, S. Wang, T. Le, D. Lee and H. Liu In particular, we propose to generate stylized headlines from original documents with style transfer.
48 Multi-task Sparse Metric Learning for Monitoring Patient Similarity Progression Q. Suo, W. Zhong, F. Ma, Y. Ye, M. Huai and A. Zhang To tackle the aforementioned challenges, we propose mtTSML, a multi-task triplet constrained sparse metric learning method, to monitor the similarity progression of patient pairs.
49 A Blended Deep Learning Approach for Predicting User Intended Actions F. Tan et al. In this work, we focus on predicting attrition, which is one of typical user intended actions.
50 Interactive Unknowns Recommendation in E-Learning Systems S. Teng, J. Li, L. P. Ting, K. Chuang and H. Liu In this paper, we address an important issue on the exploration of ‘user unknowns’ from an interactive question-answering process in E-learning systems.
51 The Impact of Environmental Stressors on Human Trafficking S. Tomkins, G. Farnadi, B. Amanatullah, L. Getoor and S. Minton We propose three theories of how these catastrophic storms might impact trafficking and provide evidence for each.
52 Multi-label Answer Aggregation Based on Joint Matrix Factorization J. Tu, G. Yu, C. Domeniconi, J. Wang, G. Xiao and M. Guo In this paper, we introduce a Multi-Label answer aggregation approach based on Joint Matrix Factorization (ML-JMF).
53 Semi-Supervised Anomaly Detection with an Application to Water Analytics V. Vercruyssen, W. Meert, G. Verbruggen, K. Maes, R. B�umer and J. Davis In this paper, we propose a novel constrained-clustering-based approach for anomaly detection that works in both an unsupervised and semi-supervised setting.
54 Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features J. Wang, Y. Gao, A. Z�fle, J. Yang and L. Zhao To address these difficulties simultaneously, we propose a novel chain-structure Multi-task Learning framework with Uncertainty Estimation (MLUE) to predict potentially victorious petitions, which facilitates the process of decision making.
55 Human-Centric Urban Transit Evaluation and Planning G. Wu, Y. Li, J. Bao, Y. Zheng, J. Ye and J. Luo In this paper, we make the first attempt to model passengers’ preferences of making various transit choices using a Markov Decision Process (MDP).
56 A United Approach to Learning Sparse Attributed Network Embedding H. Wang et al. To that end, in this paper, we propose a novel Sparse Attributed Network Embedding (SANE) framework to learn the network structure and sparse attribute information simultaneously in a united approach.
57 Deep Structure Learning for Fraud Detection H. Wang, C. Zhou, J. Wu, W. Dang, X. Zhu and J. Wang In this paper, we propose a novel deep structure learning model named DeepFD to differentiate normal users and suspicious users.
58 ASTM: An Attentional Segmentation Based Topic Model for Short Texts J. Wang, L. Chen, L. Qin and X. Wu Therefore, we propose a novel model, Attentional Segmentation based Topic Model (ASTM), to integrate both word embeddings as supplementary information and an attention mechanism that segments short text documents into fragments of adjacent words receiving similar attention.
59 A Reinforcement Learning Framework for Explainable Recommendation X. Wang, Y. Chen, J. Yang, L. Wu, Z. Wu and X. Xie To solve these problems, we design a reinforcement learning framework for explainable recommendation.
60 Exploiting Topic-Based Adversarial Neural Network for Cross-Domain Keyphrase Extraction Y. Wang et al. To this end, in this paper, we propose a novel Topic-based Adversarial Neural Network (TANN) method, which aims at exploiting the unlabeled data in the target domain and the data in the resource-rich source domain.
61 Bug Localization via Supervised Topic Modeling Y. Wang et al. In this paper, we propose a supervised topic modeling method (STMLOCATOR) for automatically locating the relevant source files for a given bug report.
62 Deep Reinforcement Learning with Knowledge Transfer for Online Rides Order Dispatching Z. Wang, Z. Qin, X. Tang, J. Ye and H. Zhu In this work, we model the ride dispatching problem as a Markov Decision Process and propose learning solutions based on deep Q-networks with action search to optimize the dispatching policy for drivers on ride-sharing platforms.
63 A Low Rank Weighted Graph Convolutional Approach to Weather Prediction T. Wilson, P. Tan and L. Luo This paper presents a novel deep learning approach based on a coupled weighted graph convolutional LSTM (WGC-LSTM) to address these challenges.
64 Robust Cascade Reconstruction by Steiner Tree Sampling H. Xiao, C. Aslay and A. Gionis For the latter problem we propose two novel algorithms with provable guarantees on the sampling distribution of the returned Steiner trees.
65 Dr. Right!: Embedding-Based Adaptively-Weighted Mixture Multi-classification Model for Finding Right Doctors with Healthcare Experience Data X. Xu et al. In this paper, we study the problem of finding high-rated doctors for a specific disease using imbalanced and heterogeneous healthcare experience rating data.
66 Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights C. Yang, Y. Feng, P. Li, Y. Shi and J. Han In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner.
67 Towards Interpretation of Recommender Systems with Sorted Explanation Paths F. Yang, N. Liu, S. Wang and X. Hu In this paper, we propose a post-hoc method called Sorted Explanation Paths (SEP) to interpret recommendation results.
68 LEEM: Lean Elastic EM for Gaussian Mixture Model via Bounds-Based Filtering S. Yang and X. Shen They are named Elastic EM in this paper.
69 Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis S. Yang and H. Koeppl In this paper we present an efficient collapsed variational inference (CVI) algorithm for the nonparametric Bayesian group factor analysis (NGFA) model built upon an hierarchical beta Bernoulli process.
70 DE-RNN: Forecasting the Probability Density Function of Nonlinear Time Series K. Yeo, I. Melnyk, N. Nguyen and E. K. Lee A regularized cross-entropy method is introduced to impose a smoothness condition on the estimated probability distribution.
71 Online Dictionary Learning with Confidence S. You, C. Xu and C. Xu Different from classical online dictionary learning methods that treat all atoms equally, in this paper, we present a novel online dictionary learning with a confidence parameter introduced on each of atoms.
72 MuVAN: A Multi-view Attention Network for Multivariate Temporal Data Y. Yuan et al. Towards this end, we propose a novel multi-view attention network, namely MuVAN, to learn fine-grained attentional representations from multivariate temporal data.
73 Adversarially Learned Anomaly Detection H. Zenati, M. Romain, C. Foo, B. Lecouat and V. Chandrasekhar In this work, we propose an anomaly detection method, Adversarially Learned Anomaly Detection (ALAD) based on bi-directional GANs, that derives adversarially learned features for the anomaly detection task.
74 SINE: Scalable Incomplete Network Embedding D. Zhang, J. Yin, X. Zhu and C. Zhang In this paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs.
75 Image-Enhanced Multi-level Sentence Representation Net for Natural Language Inference K. Zhang et al. To this end, we propose an Image-Enhanced Multi-Level Sentence Representation Net (IEMLRN), a novel architecture that is able to utilize the image to enhance the sentence semantic understanding at different scales.
76 CADEN: A Context-Aware Deep Embedding Network for Financial Opinions Mining L. Zhang, K. Xiao, H. Zhu, C. Liu, J. Yang and B. Jin To this end, we propose a context-aware deep embedding network for financial text mining, named CADEN, by jointly encoding the global and local contextual information.
77 Integrative Analysis of Patient Health Records and Neuroimages via Memory-Based Graph Convolutional Network X. Zhang, J. Chou and F. Wang In this paper, we proposed a framework, Memory-Based Graph Convolution Network (MemGCN), to perform integrative analysis with such multi-modal data.
78 Chinese Medical Concept Normalization by Using Text and Comorbidity Network Embedding Y. Zhang, X. Ma and G. Song In this paper, we propose DUNE, Disease Unsupervised Normalization by Embedding, an unsupervised Chinese medical concept normalization framework by applying denoising auto-encoder (DAE) and network embedding.
79 Billion-Scale Network Embedding with Iterative Random Projection Z. Zhang, P. Cui, H. Li, X. Wang and W. Zhu To address these problems, we propose RandNE (Iterative Random Projection Network Embedding), a novel and simple billion-scale network embedding method.
80 Zero-Shot Learning: An Energy Based Approach T. Zhao, G. Liu, L. wu, C. Ma and E. Chen To tackle these problems, in this paper, we propose an Energy-Based Zero-shot Learning model (EBZL) to encode the association between class attributes and input images for zero-shot learning.
81 Deep Learning Based Scalable Inference of Uncertain Opinions X. Zhao, F. Chen and J. Cho We propose a deep learning (DL)-based opinion inference model while node-level opinions are still formalized based on SL.
82 Dynamic Truth Discovery on Numerical Data S. Zhi, F. Yang, Z. Zhu, Q. Li, Z. Wang and J. Han We propose a model named EvolvT for dynamic truth discovery on numerical data.
83 Independent Feature and Label Components for Multi-label Classification Y. Zhong, C. Xu, B. Du and L. Zhang In contrast, this paper suggests to learn separated subspaces for features and labels by maximizing the independence between components in each subspace and maximizing the correlation between these two subspaces.
84 Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds Y. Zhu, C. M. Yeh, Z. Zimmerman, K. Kamgar and E. Keogh In this work we introduce SCRIMP++, an O(n2) time algorithm that is also an anytime algorithm, combining the best features of STOMP and STAMP.
85 Fast Rectangle Counting on Massive Networks R. Zhu, Z. Zou and J. Li We propose a novel counting paradigm called the wedge-centric counting, where a wedge is a simple path consisting of three vertices.
86 NetGist: Learning to Generate Task-Based Network Summaries S. E. Amiri, B. Adhikari, A. Bharadwaj and B. A. Prakash In this paper, we explore a promising alternative approach instead.
87 Maximizing the Diversity of Exposure in a Social Network C. Aslay, A. Matakos, E. Galbrun and A. Gionis In this paper, we take a step in this direction and propose a novel approach to maximize the diversity of exposure in a social network.
88 Semi-Supervised Community Detection Using Structure and Size A. Bakshi, S. Parthasarathy and K. Srinivasan In this paper, we design a way to represent communities concisely in an easy to compute feature space.
89 Heterogeneous Hyper-Network Embedding I. M. Baytas, C. Xiao, F. Wang, A. K. Jain and J. Zhou In this study, a deep approach is proposed to embed heterogeneous attributed hyper-networks with complicated and non-linear node relationships.
90 Accurate Causal Inference on Discrete Data K. Budhathoki and J. Vreeken In this paper we propose to use Shannon entropy to measure the dependence within an ANM, which gives us a general approach by which we do not have to assume a true distribution, nor have to perform explicit significance tests during optimization.
91 A Variable-Order Regime Switching Model to Identify Significant Patterns in Financial Markets P. Chatigny, R. Chen, J. Patenaude and S. Wang In this paper, we propose a novel RS model to identify and predict regimes based on a weighted conditional probability distribution (WCPD) framework capable of discovering and exploiting the significant underlying patterns in time series.
92 Exploiting Spatio-Temporal Correlations with Multiple 3D Convolutional Neural Networks for Citywide Vehicle Flow Prediction C. Chen et al. In this paper, we propose to apply 3D CNNs to learn the spatio-temporal correlation features jointly from low-level to high-level layers for traffic data.
93 DrugCom: Synergistic Discovery of Drug Combinations Using Tensor Decomposition H. Chen and J. Li We propose DrugCom, a tensor-based framework for computing drug combinations across different diseases by integrating multiple heterogeneous data sources of drugs and diseases.
94 Cost Effective Multi-label Active Learning via Querying Subexamples X. Chen, G. Yu, C. Domeniconi, J. Wang, Z. Li and Z. Zhang Based on this observation, we propose a novel two-stage cost effective multi-label active learning framework, called CMAL.
95 Semi-Convex Hull Tree: Fast Nearest Neighbor Queries for Large Scale Data on GPUs Y. Chen et al. A fast exact nearest neighbor search algorithm over large scale data is proposed based on semi-convex hull tree, where each node represents a semi-convex hull, which is made of a set of hyper planes.
96 Dynamic Illness Severity Prediction via Multi-task RNNs for Intensive Care Unit W. Chen, S. Wang, G. Long, L. Yao, Q. Z. Sheng and X. Li In this paper, we propose a novel approach that simultaneously analyses different organ systems to predict the illness severity of patients in an ICU, which can intuitively reflect the condition of the patients in a timely fashion.
97 Discovering Topical Interactions in Text-Based Cascades Using Hidden Markov Hawkes Processes J. Choudhari, A. Dasgupta, I. Bhattacharya and S. Bedathur We propose a Gibbs sampling algorithm for HMHP that jointly infers the network strengths, diffusion paths, the topics of the posts as well as the topic-topic interactions.
98 Signed Graph Convolutional Networks T. Derr, Y. Ma and J. Tang Therefore we propose a dedicated and principled effort that utilizes balance theory to correctly aggregate and propagate the information across layers of a signed GCN model.
99 Outlier Detection in Urban Traffic Flow Distributions Y. Djenouri, A. Zimek and M. Chiarandini Therefore, we propose to consider the sequence of traffic flow values observed within some time interval.
100 The HyperKron Graph Model for Higher-Order Features N. Eikmeier, A. Ramani and D. Gleich In this manuscript we present the HyperKron Graph model: an extension of the Kronecker Model, but with a distribution over hyperedges.
101 Using Balancing Terms to Avoid Discrimination in Classification S. Enni and I. Assent In this article, we present the Balancing Terms (BT) method to address this problem.
102 SedanSpot: Detecting Anomalies in Edge Streams D. Eswaran and C. Faloutsos We propose SedanSpot, a principled randomized algorithm, which exploits two tell-tale signs of anomalous edges: they tend to (i) occur as bursts of activity and (ii) connect parts of the graph which are sparsely connected.
103 Heterogeneous Data Integration by Learning to Rerank Schema Matches A. Gal, H. Roitman and R. Shraga In this work we propose a learning algorithm that utilizes an innovative set of features to rerank a list of schema matches and improves upon the ranking of the best match.
104 Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios S. Gharghabi, S. Imani, A. Bagnall, A. Darvishzadeh and E. Keogh In this work, we introduce a novel distance measure MPdist.
105 DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora R. Giaquinto and A. Banerjee To overcome such challenges, we adapt new ideas in approximate inference to the DAP model, resulting in the DAP Performed Exceedingly Rapidly (DAPPER) topic model.
106 Multi-level Hypothesis Testing for Populations of Heterogeneous Networks G. Gomes, V. Rao and J. Neville We propose a hierarchical Bayesian hypothesis testing framework that models each population with a mixture of latent space models for weighted networks, and then tests populations of networks for differences in distribution over components.Our framework is capable of population-level, entity-specific, as well as edge-specific hypothesis testing.
107 Multi-view Feature Selection for Heterogeneous Face Recognition J. Gui and P. Li In this paper, we propose a multi-view feature selection method (MvFS) for HFR.
108 Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders T. Guo, A. Bifet and N. Antulov-Fantulin In this paper, we study the problem of the Bitcoin short-term volatility forecasting based on volatility history and order book data.
109 Differentially Private Prescriptive Analytics H. Harikumar, S. Rana, S. Gupta, T. Nguyen, R. Kaimal and S. Venkatesh We propose a privacy preserving prescriptive analytics algorithm to protect the data used during the construction of the prescriptive analytics algorithm. In absence of any analytical form, we construct a nested global optimization problem to compute the sensitivity.
110 A General Cross-Domain Recommendation Framework via Bayesian Neural Network J. He, R. Liu, F. Zhuang, F. Lin, C. Niu and Q. He Along this line, we propose a general cross-domain recommendation framework via Bayesian neural network to incorporate auxiliary information, which takes advantage of both the hybrid recommendation methods and the cross-domain recommendation systems.
111 A Self-Organizing Tensor Architecture for Multi-view Clustering L. He et al. In this paper, we introduce a tensor-based approach to incorporate the higher-order interactions among multiple views as a tensor structure.
112 Estimating Latent Relative Labeling Importances for Multi-label Learning S. He, L. Feng and L. Li In this paper, we propose a novel multi-label learning approach that aims to estimate the latent labeling importances while training the inductive model simultaneously.
113 Characteristic Subspace Learning for Time Series Classification Y. He, J. Pei, X. Chu, Y. Wang, Z. Jin and G. Peng This paper presents a novel time series classification algorithm.
114 Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation Y. He, H. Chen, Z. Zhu and J. Caverlee We propose PsiRec, a novel user preference propagation recommender that incorporates pseudo-implicit feedback for enriching the original sparse implicit feedback dataset.
115 Confident Kernel Sparse Coding and Dictionary Learning B. Hosseini and B. Hammer In this work, we propose a novel confident K-SRC and dictionary learning algorithm (CKSC) which focuses on the discriminative reconstruction of the data based on its representation in the kernel space.
116 Highly Parallel Sequential Pattern Mining on a Heterogeneous Platform Y. Hsieh, C. Chen, H. Shuai and M. Chen In this paper, we show that a heterogeneous platform with CPU and GPU is more suitable for sequential pattern mining than traditional CPU-based approaches since the support counting process is inherently succinct and repetitive.
117 A Harmonic Motif Modularity Approach for Multi-layer Network Community Detection L. Huang, C. Wang and H. Chao In this paper, we propose a higher-order structural approach for multi-layer network community detection, termed harmonic motif modularity (HM-Modularity).
118 Learning Semantic Features for Software Defect Prediction by Code Comments Embedding X. Huo, Y. Yang, M. Li and D. Zhan In this paper, we propose a novel defect prediction model named CAP-CNN (Convolutional Neural Network for Comments Augmented Programs), which is a deep learning model that automatically embeds code comments in generating semantic features from the source code for software defect prediction.
119 DeepDiffuse: Predicting the ‘Who’ and ‘When’ in Cascades M. R. Islam, S. Muthiah, B. Adhikari, B. A. Prakash and N. Ramakrishnan We study in this paper the problem of cascade prediction utilizing only two types of (coarse) information, viz. which node is infected and its corresponding infection time.
120 Interpretable Word Embeddings for Medical Domain K. Jha, Y. Wang, G. Xun and A. Zhang To address this issue, in this study, we aim to improve the interpretability of pre-trained word embeddings generated from a text corpora, and in doing so provide a systematic approach to formalize the problem.
121 FI-GRL: Fast Inductive Graph Representation Learning via Projection-Cost Preservation F. Jiang, L. Zheng, J. Xu and P. Yu In this paper, we present a Fast Inductive Graph Representation Learning framework (FI-GRL) to learn nodes’ low-dimensional representations.
122 Mixed Bagging: A Novel Ensemble Learning Framework for Supervised Classification Based on Instance Hardness A. Kabir, C. Ruiz and S. A. Alvarez We introduce a novel ensemble learning framework for supervised classification.
123 Clustering on Sparse Data in Non-overlapping Feature Space with Applications to Cancer Subtyping T. Kang, K. Zarringhalam, M. Kuijjer, P. Chen, J. Quackenbush and W. Ding This paper presents a new algorithm, Reinforced and Informed Network-based Clustering(RINC), for finding unknown groups of similar data objects in sparse and largely non-overlapping feature space where a network structure among features can be observed.
124 Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps T. Katsuki et al. This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution.
125 Volatility Drift Prediction for Transactional Data Streams Y. S. Koh, D. T. J. Huang, C. Pearce and G. Dobbie In this paper, we propose a novel drift detection technique, ProChange, that has two parts.
126 Summarizing Graphs at Multiple Scales: New Trends D. Koutra, J. Vreeken and F. Bonchi The objective of this tutorial is to give a systematic overview of methods for summarizing and explaining graphs at different scales: the node-group level, the network level, and the multi-network level.
127 Fast Tucker Factorization for Large-Scale Tensor Completion D. Lee, J. Lee and H. Yu This paper proposes FTcom, a fast and scalable Tucker factorization method for tensor completion.
128 Diagnosis Prediction via Medical Context Attention Networks Using Deep Generative Modeling W. Lee, S. Park, W. Joo and I. Moon To address the issue, we aim at investigating the use of an attention mechanism that is tailored to medical context to predict a future diagnosis.
129 Next Point-of-Interest Recommendation with Temporal and Multi-level Context Attention R. Li, Y. Shen and Y. Zhu In this paper, we introduce a novel neural network model named TMCA (Temporal and Multi-level Context Attention) for next POI recommendation.
130 Transfer Hawkes Processes with Content Information T. Li, P. Wei and Y. Ke In this paper, we propose a novel model called transfer Hybrid Least Square for Hawkes (trHLSH) that incorporates Hawkes processes with content and cross-domain information.
131 DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN S. K. Lim, Y. Loo, N. Tran, N. Cheung, G. Roig and Y. Elovici In this paper, we instead focus on unsupervised anomaly detection and propose a novel generative data augmentation framework optimized for this task.
132 Privacy-Preserving Multi-task Learning K. Liu, N. Uplavikar, W. Jiang and Y. Fu Thus, the goal of this paper is to develop a provable privacy-preserving multi-task learning (PP-MTL) protocol that incorporates the state of the art cryptographic techniques to achieve the best security guarantee.
133 Distribution Preserving Multi-task Regression for Spatio-Temporal Data X. Liu, P. Tan, Z. Abraham, L. Luo and P. Hatami To overcome this challenge, this paper presents a novel, distribution-preserving multi-task learning framework for multi-location prediction of spatio-temporal data.
134 TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks X. Liu, X. Kong, L. Liu and K. Chiang We propose a novel GAN framework, namely TreeGAN, to incorporate a given Context-Free Grammar (CFG) into the sequence generation process.
135 Deep Discriminative Features Learning and Sampling for Imbalanced Data Problem Y. Liu, C. Liu and S. Tseng Most resampling methods for synthetic data focus on minority class without considering the data distribution of major classes.
136 D-CARS: A Declarative Context-Aware Recommender System R. Lumbantoruan, X. Zhou, Y. Ren and Z. Bao In this paper, we propose a new type of recommender system, Declarative Context-Aware Recommender System (D-CARS), which enables the personalization of the contexts exploited for each target user by automatically analysing the viewing history of users.
137 Leveraging Hypergraph Random Walk Tag Expansion and User Social Relation for Microblog Recommendation H. Ma, D. Zhang, W. Zhao, Y. Wang and Z. Shi In this work, we propose a microblog recommendation approach via hypergraph random walk tag expansion and user social relation.
138 Deep Heterogeneous Autoencoders for Collaborative Filtering T. Li, Y. Ma, J. Xu, B. Stenger, C. Liu and Y. Hirate We propose a model that learns a shared feature space from heterogeneous data, such as item descriptions, product tags and online purchase history, to obtain better predictions.
139 Text Segmentation on Multilabel Documents: A Distant-Supervised Approach S. Manchanda and G. Karypis In this paper, we develop an approach that instead of using segment-level ground truth information, it instead uses the set of labels that are associated with a document and are easier to obtain as the training data essentially corresponds to a multilabel dataset.
140 Spatial Contextualization for Closed Itemset Mining A. Mantuan and L. Fernandes We present the Spatial Contextualization for Closed Itemset Mining (SCIM) algorithm, an approach that builds a space for the target database in such a way that relevant itemsets can be retrieved regarding the relative spatial location of their items.
141 Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing S. Minn, Y. Yu, M. C. Desmarais, F. Zhu and J. Vie In this paper, we propose a novel model for knowledge tracing that i) captures students’ learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing.
142 Robust Densest Subgraph Discovery A. Miyauchi and A. Takeda In this study, we provide a framework for dense subgraph discovery under the uncertainty of edge weights.
143 Improving Deep Forest by Confidence Screening M. Pang, K. Ting, P. Zhao and Z. Zhou In this paper, we propose a simple yet effective approach to improve the efficiency of deep forest.
144 Query-Efficient Black-Box Attack by Active Learning L. Pengcheng, J. Yi and L. Zhang In this paper, we focus on the black-box attack setting where attackers have almost no access to the underlying models.
145 Predicted Edit Distance Based Clustering of Gene Sequences S. Pramanik, A. T. Islam and S. Sural In this paper we propose a predicted Edit distance based clustering to significantly lower clustering time.
146 Tracking and Forecasting Dynamics in Crowdfunding: A Basis-Synthesis Approach X. Ren, L. Xu, T. Zhao, C. Zhu, J. Guo and E. Chen To tackle this problem, we propose a novel method based on synthesized bases which can be composed into arbitrary patterns. Concretely, we build a large set of candidate basis from which we select based on reliability, diversity and latent structures.
147 Demographic Inference Via Knowledge Transfer in Cross-Domain Recommender Systems J. Shang, M. Sun and K. Collins-Thompson We introduce a novel probabilistic matrix factorization model for demographic transfer that enables knowledge transfer from the source domain, in which users’ ratings and the corresponding demographics are available, to the target domain, in which we would like to infer unknown user demographics from ratings.
148 T2S: Domain Adaptation Via Model-Independent Inverse Mapping and Model Reuse Z. Shen and M. Li In this paper, we provide an alternative way for domain adaptation, named T2S.
149 An Efficient Many-Class Active Learning Framework for Knowledge-Rich Domains W. Shi and Q. Yu In this paper, we propose a cost-effective active learning framework to further lessen human efforts, especially in knowledge-rich domains where a large number of classes may be subject to scrutiny during decision making.
150 Record2Vec: Unsupervised Representation Learning for Structured Records A. Y. L. Sim and A. Borthwick Here, we introduce Record2Vec, a framework for generating dense embeddings of structured records by training associations between attributes within record instances.
151 Multi-label Adversarial Perturbations Q. Song, H. Jin, X. Huang and X. Hu To bridge the gap, in this paper, we propose a general attacking framework targeting multi-label classification problem and conduct a premier analysis on the perturbations for deep neural networks.
152 Clustered Lifelong Learning Via Representative Task Selection G. Sun, Y. Cong, Y. Kong and X. Xu In comparison with most state-of-the-arts which adopt knowledge library with prescribed size, in this paper, we propose a new incremental clustered lifelong learning model with two libraries: feature library and model library, called Clustered Lifelong Learning (CL3), in which the feature library maintains a set of learned features common across all the encountered tasks, and the model library is learned by identifying and adding representative models (clusters).
153 Entire Regularization Path for Sparse Nonnegative Interaction Model M. Takayanagi, Y. Tabei and H. Saigo This paper presents a solution to this problem by combining itemset mining with non-negative least squares.
154 Doc2Cube: Allocating Documents to Text Cube Without Labeled Data F. Tao et al. We propose Doc2Cube, a method that constructs a text cube from a given text corpus in an unsupervised way.
155 Graph Pattern Mining and Learning through User-Defined Relations C. H. C. Teixeira, L. Cotta, B. Ribeiro and W. Meira In this work we propose R-GPM, a parallel computing framework for graph pattern mining (GPM) through a user-defined subgraph relation.
156 Robust Distributed Anomaly Detection Using Optimal Weighted One-Class Random Forests Y. Tsou, H. Chu, C. Li and S. Yang In this paper, we propose a novel framework using optimal weighted one-class random forests for unsupervised anomaly detection to address the aforementioned challenges in WSNs.
157 Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion V. Uurtio, S. Bhadra and J. Rousu We present SCCA-HSIC, a method for finding sparse non-linear multivariate relations in high-dimensional settings by maximizing the Hilbert-Schmidt Independence Criterion (HSIC).
158 Imputing Structured Missing Values in Spatial Data with Clustered Adversarial Matrix Factorization Q. Wang, P. Tan and J. Zhou To address these limitations, this paper presents a novel clustered adversarial matrix factorization method to explore and exploit the underlying cluster structure of the spatial data in order to facilitate effective imputation.
159 Partial Multi-view Clustering via Consistent GAN Q. Wang, Z. Ding, Z. Tao, Q. Gao and Y. Fu In this paper, a new consistent generative adversarial network is proposed for partial multi-view clustering.
160 EPAB: Early Pattern Aware Bayesian Model for Social Content Popularity Prediction Q. Wu, C. Yang, X. Gao, P. He and G. Chen In this paper, we take a fresh perspective, and propose a novel early pattern aware Bayesian model.
161 DeepAD: A Deep Learning Based Approach to Stroke-Level Abnormality Detection in Handwritten Chinese Character Recognition T. Wang and C. Liu Considering that abnormally written strokes (writing error or largely distorted stroke) affect the decision confidence of classifier, we propose an approach named DeepAD to detect stroke-level abnormalities in handwritten Chinese characters by analyzing the decision process of deep neural network (DNN). For quantitative evaluation of performance, we build a template-free dataset named SA-CASIA-HW containing 3696 handwritten Chinese characters with various stroke-level abnormalities, and spanning 3000+ different classes written by 60 individual writers.
162 Multiple Co-clusterings X. Wang, G. Yu, C. Domeniconi, J. Wang, Z. Yu and Z. Zhang To tackle this challenge and unexplored problem, in this paper we introduce an approach, called Multiple Co-Clusterings (MultiCC), to discover non-redundant alternative co-clusterings.
163 Uncluttered Domain Sub-Similarity Modeling for Transfer Regression P. Wei, R. Sagarna, Y. Ke and Y. S. Ong We focus on regression problems in a black-box learning scenario, and study a family of rather general transfer covariance functions, T_*, that can model the similarity heterogeneity of domains through multiple kernel learning.
164 Finding Maximal Significant Linear Representation between Long Time Series J. Wu, Y. Wang, P. Wang, J. Pei and W. Wang To tackle the challenges in measuring linear correlation between two long time series, in this paper, we formulate the novel problem of finding maximal significant linear representation.
165 eOTD: An Efficient Online Tucker Decomposition for Higher Order Tensors H. Xiao, F. Wang, F. Ma and J. Gao In this paper, we propose an efficient Online Tucker Decomposition (eOTD) approach to track the TD of dynamic tensors with an arbitrary number of modes.
166 Prediction of MicroRNA Subcellular Localization by Using a Sequence-to-Sequence Model Y. Xiao, J. Cai, Y. Yang, H. Zhao and H. Shen In this study, we regard this prediction task as a Sequence-to-Sequence learning process and propose an attention-based encoder-decoder model, miRLocator, to identify subcellular locations of human miRNAs.
167 Unsupervised User Identity Linkage via Factoid Embedding W. Xie, X. Mu, R. K. Lee, F. Zhu and E. Lim In this paper, we propose Factoid Embedding, a novel framework that adopts an unsupervised approach.
168 A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets Y. Xie, H. Bao, S. Shekhar and J. Knight We propose a TIMBER framework to find individual trees in complex urban environments and a Core Object REduction (CORE) algorithm to improve the computational efficiency of TIMBER.
169 Active Learning on Heterogeneous Information Networks: A Multi-armed Bandit Approach D. Xin, A. El-Kishky, D. Liao, B. Norick and J. Han We investigate active learning on heterogeneous information networks, with the objective of obtaining accurate node classifications while minimizing the number of labeled nodes.
170 Exploiting the Sentimental Bias between Ratings and Reviews for Enhancing Recommendation Y. Xu, Y. Yang, J. Han, E. Wang, F. Zhuang and H. Xiong To this end, in this paper, we develop an opinion mining model based on convolutional neural networks for enhancing recommendation (NeuO).
171 Enhancing Question Understanding and Representation for Knowledge Base Relation Detection Z. Xu, H. Zheng, Z. Fu and W. Wang In this paper, we propose a novel system with enhanced question understanding and representation processes for KB relation detection (QURRD).
172 A Knowledge-Enhanced Deep Recommendation Framework Incorporating GAN-Based Models D. Yang, Z. Guo, Z. Wang, J. Jiang, Y. Xiao and W. Wang In this paper, towards movie recommendation, we propose a novel knowledge-enhanced deep recommendation framework incorporating GAN-based models to acquire robust performance.
173 Adaptive Affinity Learning for Accurate Community Detection F. Ye, S. Li, Z. Lin, C. Chen and Z. Zheng In this paper, we propose to learn an affinity matrix adaptively, which can capture the intrinsic similarity between nodes accurately, and therefore benefit the community detection results.
174 A Unified Theory of the Mobile Sequential Recommendation Problem Z. Ye, K. Xiao and Y. Deng A theory is developed to unify the original form, and its many variations, of the mobile sequential recommendation (MSR) problem.
175 An Integrated Model for Crime Prediction Using Temporal and Spatial Factors F. Yi, Z. Yu, F. Zhuang, X. Zhang and H. Xiong In this paper, we propose a Clustered Continuous Conditional Random Field (Clustered-CCRF) model which is able to effectively exploit both spatial and temporal factors for crime prediction in an integrated way.
176 Coherent Graphical Lasso for Brain Network Discovery H. Yin, X. Kong and X. Liu In this paper, we study the problem of collective discovery of coherent brain regions and direct connections between these regions.
177 Feature-Induced Partial Multi-label Learning G. Yu et al. To tackle the PML challenge, we introduce a feature induced PML approach called fPML, which simultaneously estimates noisy labels and trains multi-label classifiers.
178 Superlinear Convergence of Randomized Block Lanczos Algorithm Q. Yuan, M. Gu and B. Li In this paper, we present a unified singular value convergence analysis for this algorithm, for all valid choices of the block size.
179 Neural Sentence-Level Sentiment Classification with Heterogeneous Supervision Z. Yuan, F. Wu, J. Liu, C. Wu, Y. Huang and X. Xie In this paper, we propose a neural sentence-level sentiment classification approach which can exploit heterogeneous sentiment supervision and reduce the dependence on labeled sentences.
180 A Machine Reading Comprehension-Based Approach for Featured Snippet Extraction C. Zhang, X. Zhang and H. Wang Based on that, we present a model to extract the candidate passages from recalled documents in a MRC fashion.
181 Similarity-Based Active Learning for Image Classification Under Class Imbalance C. Zhang, W. Tavanapong, G. Kijkul, J. Wong, P. C. de Groen and J. Oh In this paper, we propose a novel similarity-based active deep learning framework (SAL) that deals with class imbalance.
182 Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction J. Zhang, J. Wang, L. He, Z. Li and P. S. Yu In this paper, we use SMART attributes to predict hard drive health degrees which are helpful for taking different fault tolerant actions in advance.
183 Heterogeneous Embedding Propagation for Large-Scale E-Commerce User Alignment V. W. Zheng et al. To address the challenges, we propose a novel Heterogeneous Embedding Propagation (HEP) model.
184 Robust Regression via Online Feature Selection Under Adversarial Data Corruption X. Zhang, S. Lei, L. Zhao, A. Boedihardjo and C. Lu This paper proposes a novel RObust regression algorithm via Online Feature Selection (RoOFS) that concurrently addresses all the above challenges.
185 Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion Z. Zhang and C. Hawkins This paper presents a Bayesian robust streaming tensor factorization model to identify sparse outliers, automatically determine the underlying tensor rank and accurately fit low-rank structure.
186 Forecasting Wavelet Transformed Time Series with Attentive Neural Networks Y. Zhao, Y. Shen, Y. Zhu and J. Yao Inspired by the recent advent of signal processing and speech recognition techniques that decompose a time series signal into its time-frequency representation – a scalogram (or spectrogram), this paper proposes to explicitly disclose frequency-domain information from a univariate time series using wavelet transform, towards improving forecasting accuracy.
187 Online CP Decomposition for Sparse Tensors S. Zhou, S. Erfani and J. Bailey To address this gap, we propose a new incremental algorithm for tracking the CP decompositions of online sparse tensors on-the-fly.
188 Density-Adaptive Local Edge Representation Learning with Generative Adversarial Network Multi-label Edge Classification Y. Zhou et al. This paper presents a novel edge representation learning framework, GANDLERL, that combines generative adversarial network based multi-label classification with density-adaptive local edge representation learning for producing high-quality low-dimensional edge representations.
189 Evaluating Top-k Meta Path Queries on Large Heterogeneous Information Networks Z. Zhu, R. Cheng, L. Do, Z. Huang and H. Zhang We propose a solution that seamlessly integrates these functions into an A* search framework.
190 Binarized attributed network embedding H. Yang, S. Pan, P. Zhang, L. Chen, D. Lian and C. Zhang To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation.