2026
Normality Calibration in Semi-supervised Graph Anomaly Detection
ICML 2026poster
Semi-supervised graph anomaly detection (GAD), which assumes a subset of annotated normal nodes available during training, is among the most widely explored applications. However, the normality learned by existing semi-supervised GAD methods is limited to the labeled normal nodes, often inclining to…