Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks in node classification, link prediction, and graph classification thanks to the highly expressive capability via message passing in effectively learning graph representations. To solve the graph anomaly detection problem, GNN-based methods leverage information about the graph attributes (or features) and/or structures to learn to score anomalies appropriately. In this survey, we review the recent advances made in detecting graph anomalies using GNN models. Specifically, we summarize GNN-based methods according to the graph type (i.e., static and dynamic), the anomaly type (i.e., node, edge, subgraph, and whole graph), and the network architecture (e.g., graph autoencoder, graph convolutional network). To the best of our knowledge, this survey is the first comprehensive review of graph anomaly detection methods based on GNNs.

翻译：图形神经网络(GNNS)在近些年来进行了广泛的研究,并成功地在节点分类、链接预测和图形分类方面完成了困难的机器学习任务。由于通过信息传递,通过有效学习图形演示,呈现出高度清晰的能力,为了解决图形异常检测问题,基于GNN的方法利用图形特征(或特征)和/或结构的信息来适当分辨异常。在本次调查中,我们审查了在利用GNN模型探测图形异常方面的最新进展。具体地说,我们根据图形类型(即静态和动态)、异常类型(即节点、边缘、子图谱和整个图)和网络结构(例如,图形自动电离子、图形等)总结了基于GNNN的方法。