<ul><li>Graph anomaly detection (GAD) is a technique to identify abnormal nodes within a graph.</li><li>Current GAD methods require dataset-specific training, leading to high costs and limited generalizability.</li><li>ARC is a generalist GAD approach that can detect anomalies across various graph datasets on-the-fly.</li><li>ARC uses in-context learning to extract dataset-specific patterns without retraining or fine-tuning.</li></ul>

ARC: A Generalist Graph Anomaly Detector with In-Context Learning

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