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cs.LG2025
Normality Calibration in Semi-supervised Graph Anomaly Detection
Guolei Zeng, Hezhe Qiao, Guoguo Ai +2
Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications. Semi-supervised GAD, which assumes a subse…
cs.LG2025
Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
Guoguo Ai, Hezhe Qiao, Hui Yan +1
Semi-supervised graph anomaly detection (GAD) utilizes a small set of labeled normal nodes to identify abnormal nodes from a large set of unlabeled nodes in a graph. Current method…