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

This paper introduces SignGAD, a novel framework for few-shot graph anomaly detection that designs task-conditioned detection workflows instead of using fixed anomaly detectors. It addresses challenges of adaptability and weak evidence by selecting suitable graph encodings and detector designs to exploit task-specific anomaly signals.