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arXiv AI By Hezhe Qiao, Chaoxi Niu, Ling Chen, Guansong Pang

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection

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AnomalyGFM is a graph foundation model designed for zero- and few-shot anomaly detection in graphs. It learns graph‑agnostic normal and abnormal prototypes by aligning data‑independent class representations with node representation residuals, enabling consistent abnormality measurement across diverse graph datasets. Experiments on 11 real‑world GAD datasets show that AnomalyGFM outperforms state‑of‑the‑art methods in both zero‑shot and few‑shot settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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