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 https://github.com/liuyishoua/Mul-Graph-Fusion.
翻译:异常检测被定义为与预期行为不符的发现模式。以前,异常检测大多使用传统的浅度学习技术进行,但几乎没有什么改进。随着图形神经网络的出现,图形异常检测有了很大的发展。然而,最近的研究表明,基于GNN的方法遇到了挑战,因为没有图形异常检测算法能够对大多数数据集进行概括化。为了连接这一连接,我们建议采用多视聚合法来探测图异常(Mul-GAD) 。视觉级融合法捕捉不同观点之间的重要程度,而特征级融合则充分利用了补充信息。我们从理论上和实验性地阐述了聚合战略的有效性。为了更全面的结论,我们进一步调查了目标功能的影响和对检测性能的综合观点的数量。利用这些发现,我们建议Mul-GAD 配有组合战略和完善的客观功能。与其他州级检测方法相比,我们通过一系列的计算机/亚马孙系统/亚马孙号,我们通过一系列的图像/亚马孙号实验,我们得到了更好的探测和概括性功能。