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

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 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, and their pre-consensus disagreement is accumulated across ATC steps for detection. We further characterize this signal from graph-spectral and source-response perspectives and derive sufficient conditions for anomaly--normal separation under contextual mixing. Experiments on six benchmarks show the highest average AUROC among the evaluated methods, while controlled interventions and Adapt-operator controls further support persistent disagreement as an effective detection signal.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2605.26446 [cs.LG]

(or arXiv:2605.26446v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.26446 Focus to learn more arXiv-issued DOI via DataCite

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