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 ambiguous. We propose Disagreement-Driven Graph Anomaly Detection (DDGAD), which treats persistent incompatibility between node-wise and contextual estimates as anomaly evidence. Inspired by Adapt-Then-Combine (ATC), DDGAD reverses its consensus objective: Adapt produces a node-wise estimate without new same-step neighbor aggregation, while Combine forms a neighborhood-dependent contextual estimate, a...
| Authors | Yuxin Yang, Limei Hu, Feng Chen |
|---|---|
| Primary category | cs.LG |
| Published | 26 May 2026 |
| Last updated at source | 22 September 2026 |
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