Author

Bin Shi

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#machine learning May 2026

Generalist Graph Anomaly Detection via Prototype-Based Distillation

ProMoS is introduced, the first unsupervised generalist GAD framework, which detects anomalies by modeling the abundant normality in unlabeled data, and proposes prototype-guided soft-label distillation to align teacher and student in a shared prototype space, enhancing cross-graph generalizability.

Yiming Xu, Zihan Chen, Z. Peng et al. · arXiv.org · 0 citations