DDGAD: Disagreement-Driven Graph Anomaly Detection via Adapt-Then-Combine.

arXiv:2605.26446v3 Announce Type: replace-cross Abstract: Graph anomaly detection (GAD) commonly relies on message passing to jointly encode node attributes and neighborhood context. However, once the two are mixed, an abnormal post-encoding state may reflect