Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision

ArXi:2602.20019v2 Announce Type: replace-cross Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies. Existing methods are either unsupervised or semi-supervised: unsupervised methods avoid the need for labeled anomalies but often produce ambiguous boundary, whereas semi-supervised methods can overfit to the limited labeled anomalies and generalize poorly to unseen anomalies.