AI RESEARCH
Generalist Graph Anomaly Detection via Prototype-Based Distillation
arXiv CS.LG
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ArXi:2605.26857v1 Announce Type: new Driven by the pressing demand for graph anomaly detection (GAD) in high-stakes domains, the generalist GAD paradigm, which trains a single detector transferable across new graphs, has recently gained growing attention. However, existing methods often rely on scarce and costly annotations for