arXiv: cs.AI preprintsInternational6 November 2026
Towards Generalist Graph-Level Anomaly Detection
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arXiv:2611.07706v1 Announce Type: new Abstract: Graph-level anomaly detection (GLAD) aims to identify graphs that deviate significantly from the majority. While recent advances have achieved competitive performance, existing approaches mainly follow a dataset-specific paradigm, requiring substantial in-domain data and training cost when applied to new application scenarios. This limits their scalability and practicality in real-world applications where labeled data is scarce and diverse domains are continuously emerging. In this paper, we investigate the problem of generalist GLAD, which seeks
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