All Stories

  1. Workshop on Benchmarking Causal Models (CausalBench)
  2. Explaining the 'Unexplainable' Large Language Models
  3. On Causal and Anticausal LLM-based Data Synthesis
  4. Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era
  5. An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution
  6. Teaching Cybersecurity with a Trustworthy AI Assistant
  7. CausalBench-ER: Causally-Informed Explanations and Recommendations for Reproducible Benchmarking
  8. Building Safer Sites: A Large-Scale Multi-Level Dataset for Construction Safety Benchmark
  9. GraphRCG: Self-Conditioned Graph Generation
  10. AI4DE: The 1st International Workshop on AI for Data Editing
  11. RelKD 2025: The Third International Workshop on Resource-Efficient Learning for Knowledge Discovery
  12. CausalBench: Causal Learning Research Streamlined
  13. Resource-Efficient Learning for the Web
  14. Exploring Large Language Models for Feature Selection: A Data-centric Perspective
  15. Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learning
  16. RelKD 2024: The Second International Workshop on Resource-Efficient Learning for Knowledge Discovery
  17. A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation
  18. ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement
  19. (Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning
  20. Causality Guided Disentanglement for Cross-Platform Hate Speech Detection
  21. Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification
  22. Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach
  23. STREAMS: Towards Spatio-Temporal Causal Discovery with Reinforcement Learning for Streamflow Rate Prediction
  24. A Two-tier Shared Embedding Method for Review-based Recommender Systems
  25. HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
  26. GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
  27. Causal Disentanglement for Implicit Recommendations with Network Information
  28. Data Augmentation for Deep Graph Learning
  29. Toward Graph Minimally-Supervised Learning
  30. The KDD 2022 Workshop on Causal Discovery (CD2022)
  31. Bias Mitigation for Toxicity Detection via Sequential Decisions
  32. CausalSE: Understanding Varied Spatial Effects with Missing Data Towards Adding New Bike-sharing Stations
  33. Domain Adaptive Fake News Detection via Reinforcement Learning
  34. “This is Fake! Shared it by Mistake”:Assessing the Intent of Fake News Spreaders
  35. Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies
  36. Causal Mediation Analysis with Hidden Confounders
  37. Graph Minimally-supervised Learning
  38. Graph Few-shot Class-incremental Learning
  39. Towards Anomaly-resistant Graph Neural Networks via Reinforcement Learning
  40. CauseBox
  41. Profiling Fake News Spreaders on Social Media through Psychological and Motivational Factors
  42. The KDD 2021 Workshop on Causal Discovery (CD2021)
  43. Causal Understanding of Fake News Dissemination on Social Media
  44. Adversarial Attacks and Defenses
  45. Few-shot Network Anomaly Detection via Cross-network Meta-learning
  46. Improving Cyberbullying Detection with User Interaction
  47. Modeling Temporal Patterns of Cyberbullying Detection with Hierarchical Attention Networks
  48. Long-Term Effect Estimation with Surrogate Representation
  49. Graph Prototypical Networks for Few-shot Learning on Attributed Networks
  50. Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model
  51. The 5th International Workshop on Mining Actionable Insights from Social Networks (MAISoN 2020)
  52. A Survey of Learning Causality with Data
  53. Debiasing Grid-based Product Search in E-commerce
  54. Next-item Recommendation with Sequential Hypergraphs
  55. Joint Local and Global Sequence Modeling in Temporal Correlation Networks for Trending Topic Detection
  56. Causal Interpretability for Machine Learning - Problems, Methods and Evaluation
  57. A Survey on Privacy in Social Media
  58. Social Science–guided Feature Engineering
  59. #suicidal - A Multipronged Approach to Identify and Explore Suicidal Ideation in Twitter
  60. Beyond word2vec
  61. dEFEND
  62. BigScholar 2019: The 6th Workshop on Big Scholarly Data
  63. dEFEND
  64. Adaptive Unsupervised Feature Selection on Attributed Networks
  65. Learning From Networks
  66. Fake News Research
  67. Debunking Rumors in Social Networks
  68. Signed Link Prediction with Sparse Data: The Role of Personality Information
  69. Robust Cyberbullying Detection with Causal Interpretation
  70. Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items
  71. Fake News
  72. XBully
  73. Protecting User Privacy
  74. Beyond News Contents
  75. Random-Forest-Inspired Neural Networks
  76. Towards Explainable Networked Prediction
  77. Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation
  78. "Bridge"
  79. Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects
  80. Exploiting Multilabel Information for Noise-Resilient Feature Selection
  81. Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
  82. Securing Social Media User Data
  83. Identifying Framing Bias in Online News
  84. Turning Clicks into Purchases
  85. Understanding and Identifying Rhetorical Questions in Social Media
  86. CrossFire
  87. Streaming Link Prediction on Dynamic Attributed Networks
  88. Leveraging Implicit Contribution Amounts to Facilitate Microfinancing Requests
  89. Tracing Fake-News Footprints
  90. Feature Selection
  91. Understanding and Predicting Delay in Reciprocal Relations
  92. Attributed Network Embedding for Learning in a Dynamic Environment
  93. Attributed Signed Network Embedding
  94. Randomized Feature Engineering as a Fast and Accurate Alternative to Kernel Methods
  95. Unsupervised Feature Selection in Signed Social Networks
  96. What Your Images Reveal
  97. A Survey of Signed Network Mining in Social Media
  98. Leveraging the Implicit Structure within Social Media for Emergent Rumor Detection
  99. Paired Restricted Boltzmann Machine for Linked Data
  100. Linked Document Embedding for Classification
  101. Replacing Mechanical Turkers? Challenges in the Evaluation of Models with Semantic Properties
  102. Recommendations in Signed Social Networks
  103. Relational Learning with Social Status Analysis
  104. Understanding and Identifying Advocates for Political Campaigns on Social Media
  105. Toward Dual Roles of Users in Recommender Systems
  106. 10 Bits of Surprise
  107. Unsupervised Streaming Feature Selection in Social Media
  108. The Fragility of Twitter Social Networks Against Suspended Users
  109. Finding the Right Social Media Site for Questions
  110. Exploring a Scalable Solution to Identifying Events in Noisy Twitter Streams
  111. Evaluation without ground truth in social media research
  112. Sarcasm Detection on Twitter
  113. Negative Link Prediction in Social Media
  114. Text, Topics, and Turkers
  115. Surpassing the Limit
  116. Predictability of Distrust with Interaction Data
  117. When is it biased?
  118. Understanding Twitter data with TweetXplorer
  119. A tool for collecting provenance data in social media
  120. Exploiting homophily effect for trust prediction
  121. Exploiting social relations for sentiment analysis in microblogging
  122. Modeling temporal effects of human mobile behavior on location-based social networks
  123. A tool for assisting provenance search in social media
  124. Seeking provenance of information using social media
  125. Mining Social Media: A Brief Introduction