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cs.LG2025
DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
Qingfeng Chen, Haojin Zeng, Jingyi Jie +2
With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGAD seeks to identify entire graph…
cs.LG2025
Learning Fair Graph Representations with Multi-view Information Bottleneck
Chuxun Liu, Debo Cheng, Qingfeng Chen +3
Graph neural networks (GNNs) excel on relational data by passing messages over node features and structure, but they can amplify training data biases, propagating discriminatory at…