Anomaly detection is defined as discovering patterns that do not conform to
the expected behavior. Previously, anomaly detection was mostly conducted using
traditional shallow learning techniques, but with little improvement. As the
emergence of graph neural networks (GNN), graph anomaly detection has been
greatly developed. However, recent studies have shown that GNN-based methods
encounter challenge, in that no graph anomaly detection algorithm can perform
generalization on most datasets. To bridge the tap, we propose a multi-view
fusion approach for graph anomaly detection (Mul-GAD). The view-level fusion
captures the extent of significance between different views, while the
feature-level fusion makes full use of complementary information. We
theoretically and experimentally elaborate the effectiveness of the fusion
strategies. For a more comprehensive conclusion, we further investigate the
effect of the objective function and the number of fused views on detection
performance. Exploiting these findings, our Mul-GAD is proposed equipped with
fusion strategies and the well-performed objective function. Compared with
other state-of-the-art detection methods, we achieve a better detection
performance and generalization in most scenarios via a series of experiments
conducted on Pubmed, Amazon Computer, Amazon Photo, Weibo and Books. Our code
is available at this https URL.

本文提出了一种多视图融合方法用于图形异常检测，该方法结合了视角级融合和特征级融合以更好地利用不同视角的信息。在多个数据集上进行的实验表明，该方法在异常检测性能和泛化能力方面优于其他现有算法。