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Graph anomaly detection is crucial for identifying nodes that deviate fromregular behavior within graphs, benefiting various domains such as frauddetection and social network. Although existing reconstruction-based methodshave achieved considerable success, they may face the \textit{AnomalyOverfitting} and \textit{Homophily Trap} problems caused by the abnormalpatterns in the graph, breaking the assumption that normal nodes are oftenbetter reconstructed than abnormal ones. Our observations indicate that modelstrained on graphs with fewer anomalies exhibit higher detection performance.Based on this insight, we introduce a novel two-stage framework calledAnomaly-Denoised Autoencoders for Graph Anomaly Detection (ADA-GAD). In thefirst stage, we design a learning-free anomaly-denoised augmentation method togenerate graphs with reduced anomaly levels. We pretrain graph autoencoders onthese augmented graphs at multiple levels, which enables the graph autoencodersto capture normal patterns. In the next stage, the decoders are retrained fordetection on the original graph, benefiting from the multi-levelrepresentations learned in the previous stage. Meanwhile, we propose the nodeanomaly distribution regularization to further alleviate \textit{AnomalyOverfitting}. We validate the effectiveness of our approach through extensiveexperiments on both synthetic and real-world datasets.

ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection