Survey of Application of Graph Neural Network in Anomaly Detection

Graph data is commonly used to represent complex relationships between different individuals, such as social networks, financial networks, and microservice networks. Graph neural network (GNN) is a deep learning model used for processing graph data, which can effectively capture structural and feature information in graph data. Anomaly detection refers to identifying unexpected data from a massive amount of data. Traditional anomaly detection methods usually do not consider the relationships between data when detecting graph data, while models that use GNN for anomaly detection can learn from graph structures and features, thereby improving the accuracy and robustness of anomaly detection. This paper reviews the application of GNN in anomaly detection from three aspects. Firstly, the basic framework of GNN is introduced. Secondly, the latest research progress of GNN in static graph anomaly detection, dynamic graph anomaly detection, and time series data anomaly detection is discussed separately. Finally, an in-depth analysis is conducted on the future research directions in this field. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.