  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.
