论文标题
MUL-GAD：通过汇总多视图信息的半监督图异常检测框架
Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information
论文作者
论文摘要
异常检测定义为发现不符合预期行为的模式。以前，异常检测主要是使用传统的浅学习技术进行的，但几乎没有改进。随着图神经网络（GNN）的出现，图形异常检测得到了大量发展。但是，最近的研究表明，基于GNN的方法会遇到挑战，因为没有图异常检测算法可以在大多数数据集上进行概括。为了弥合水龙头，我们提出了一种多视图融合方法，用于图形异常检测（MUL-GAD）。视图融合捕获了不同观点之间的重要性程度，而功能级融合充分利用了互补信息。我们从理论上和实验上阐述了融合策略的有效性。为了得出更全面的结论，我们进一步研究了目标函数的效果以及融合观点对检测性能的影响。利用这些发现，我们的Mul-Gad提出了配备融合策略和表现出良好的目标功能。与其他最先进的检测方法相比，在大多数情况下，我们通过在PubMed，Amazon Computer，Amazon Photo，Weibo和Books上进行的一系列实验来实现更好的检测性能和概括。我们的代码可在https://github.com/liuyishoua/mul-graph-fusion上找到。
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.
