Anomaly detection on attributed graphs is essential for scientific integrity, cybersecurity, and financial oversight, where abnormal patterns often manifest as breaks in structure or attributes. However, existing unsupervised methods are difficult to combine both global and local perspectives to detect anomalies. To address this issue, we propose DCGAD, a unified unsupervised framework that captures anomalies by fusing global reconstruction error and local view inconsistency. Our model leverages diffusion reconstruction to strengthen global semantic information, employing two parallel autoencoders to reconstruct the graph structure based on the original features and diffusion-enhanced features, respectively, to capture global structural differences. Complementarily, the model samples two local subgraph views per target node and uses multi-view contrastive learning to evaluate local contextual inconsistencies. By jointly optimizing these two complementary objectives, our proposed model achieves collaborative use of local and global information. Extensive experiments on six real-world graph datasets show that DCGAD outperforms other state-of-the-art approaches, achieving excellent scores on citation networks and significant gains on social and collaborative platforms.
Hu et al. (Wed,) studied this question.
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