  Graph anomaly detection is crucial for identifying nodes that deviate from
regular behavior within graphs, benefiting various domains such as fraud
detection and social network. Although existing reconstruction-based methods
have achieved considerable success, they may face the \textit{Anomaly
Overfitting} and \textit{Homophily Trap} problems caused by the abnormal
patterns in the graph, breaking the assumption that normal nodes are often
better reconstructed than abnormal ones. Our observations indicate that models
trained on graphs with fewer anomalies exhibit higher detection performance.
Based on this insight, we introduce a novel two-stage framework called
Anomaly-Denoised Autoencoders for Graph Anomaly Detection (ADA-GAD). In the
first stage, we design a learning-free anomaly-denoised augmentation method to
generate graphs with reduced anomaly levels. We pretrain graph autoencoders on
these augmented graphs at multiple levels, which enables the graph autoencoders
to capture normal patterns. In the next stage, the decoders are retrained for
detection on the original graph, benefiting from the multi-level
representations learned in the previous stage. Meanwhile, we propose the node
anomaly distribution regularization to further alleviate \textit{Anomaly
Overfitting}. We validate the effectiveness of our approach through extensive
experiments on both synthetic and real-world datasets.
