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The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while avoiding biased predictions against individuals from sensitive subgroups.
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Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information. In WSDM . 680–688
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Racism is a virus: anti-asian hate and counterspeech in social media during the COVID-19 crisis. In ASONAM . 90–94
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Fair Graph Mining. In ACM CIKM . 4849–4852
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Adversarial Learning of Balanced Triangles for Accurate Community Detection on Signed Networks. In IEEE ICDM . 1150–1155
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Mul-GAD: A Semi-Supervised Graph Anomaly Detection Framework via Aggregating Multi-View Information
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FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias. In LREC . 3231–3241
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Examining the impact of sharing COVID-19 misinformation online on mental health
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Training fair deep neural networks by balancing influence
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Look Before You Leap: Confirming Edge Signs in Random Walk with Restart for Personalized Node Ranking in Signed Networks. In ACM SIGIR . 143–152
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Inductive representation learning in temporal networks via mining neighborhood and community influences. In SIGIR . 2202–2206
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Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
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A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
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FairOD: Fairness-aware Outlier Detection. In AIES
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Fair Representation Learning for Heterogeneous Information Networks
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Bias mitigation for evidence-aware fake news detection by causal intervention. In SIGIR . 2308–2313
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Contrastive attributed network anomaly detection with data augmentation. In PAKDD . 444–457
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Fairness in graph mining: A survey
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Fake news believability: The effects of political beliefs and espoused cultural values
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Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method. In IEEE ICDE . 2565–2578
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TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks. In ACM SIGIR . 2451–2455
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Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding. In ACM Web Conference (WWW) . 231–239
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MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms. In ACL
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A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal
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Deep Temporal Graph Clustering. In The 12th International Conference on Learning Representations
Meng Liu, Yue Liu, Ke Liang, Wenxuan Tu, Siwei Wang, Sihang Zhou, and Xinwang Liu. 2024 · 2024
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A Survey of Graph Neural Networks for Social Recommender Systems
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Competeai: Understanding the competition behaviors in large language model-based agents. In ICML
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