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Anomaly detection is defined as discovering patterns that do not conform tothe expected behavior. Previously, anomaly detection was mostly conducted usingtraditional shallow learning techniques, but with little improvement. As theemergence of graph neural networks (GNN), graph anomaly detection has beengreatly developed. However, recent studies have shown that GNN-based methodsencounter challenge, in that no graph anomaly detection algorithm can performgeneralization on most datasets. To bridge the tap, we propose a multi-viewfusion approach for graph anomaly detection (Mul-GAD). The view-level fusioncaptures the extent of significance between different views, while thefeature-level fusion makes full use of complementary information. Wetheoretically and experimentally elaborate the effectiveness of the fusionstrategies. For a more comprehensive conclusion, we further investigate theeffect of the objective function and the number of fused views on detectionperformance. Exploiting these findings, our Mul-GAD is proposed equipped withfusion strategies and the well-performed objective function. Compared withother state-of-the-art detection methods, we achieve a better detectionperformance and generalization in most scenarios via a series of experimentsconducted on Pubmed, Amazon Computer, Amazon Photo, Weibo and Books. Our codeis available at https://github.com/liuyishoua/Mul-Graph-Fusion.

Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information