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DDGAD: Disagreement-Driven Graph Anomaly Detection via Adapt-Then-Combine
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 either an intrinsic node deviation or incompatible contextual influence, making its source ambiguo
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