Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection

arXiv:2605.27470v1 Announce Type: new Abstract: Graph anomaly detection aims to identify anomaly nodes in attributed graphs and plays an important role in real-world applications. However, existing graph anomaly detection methods still face two key challenges: 1) fixed pipelines, which restrict their adaptability across different graph tasks under limited supervision; 2) weak evidence, which prevents them from explicitly incorporating contextual and structural anomaly signals into the detection process. In this paper, we propose a novel framework, self-designing agentic workflows for few-shot

Source: arXiv cs.LG — read the full report at the original publisher.

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