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This paper introduces TA-GGAD, a graph foundation model designed for generalist graph anomaly detection that addresses the "Anomaly Disassortativity" ($\mathcal{AD}$) issue arising from domain shift. TA-GGAD is trained once and adapts at testing time to different graphs, modeling the feature mismatch pattern that characterizes domain shift in graph anomaly detection. Experiments across fourteen real-world graphs demonstrate state-of-the-art cross-domain adaptation performance, validating the $\mathcal{AD}$ theory and offering a practical approach for GGAD.
A single graph foundation model can now achieve state-of-the-art anomaly detection across diverse graph domains, thanks to a new theory of "Anomaly Disassortativity" that tackles domain shift.
A significant number of anomalous nodes in the real world, such as fake news, noncompliant users, malicious transactions, and malicious posts, severely compromises the health of the graph data ecosystem and urgently requires effective identification and processing. With anomalies that span multiple data domains yet exhibit vast differences in features, cross-domain detection models face severe domain shift issues, which limit their generalizability across all domains. This study identifies and quantitatively analyzes a specific feature mismatch pattern exhibited by domain shift in graph anomaly detection, which we define as the \emph{Anomaly Disassortativity} issue ($\mathcal{AD}$). Based on the modeling of the issue $\mathcal{AD}$, we introduce a novel graph foundation model for anomaly detection. It achieves cross-domain generalization in different graphs, requiring only a single training phase to perform effectively across diverse domains. The experimental findings, based on fourteen diverse real-world graphs, confirm a breakthrough in the model's cross-domain adaptation, achieving a pioneering state-of-the-art (SOTA) level in terms of detection accuracy. In summary, the proposed theory of $\mathcal{AD}$ provides a novel theoretical perspective and a practical route for future research in generalist graph anomaly detection (GGAD). The code is available at https://anonymous.4open.science/r/Anonymization-TA-GGAD/.
