Deep Learning for Graph Anomaly Detection

Deep learning for graph anomaly detection (DLGAD), which aims to identify rare observations in graphs, has attracted rapidly increasing attention in recent years due to its significance in a wide range of high-impact application domains such as abusive review detection and malicious behavior detection in online shopping applications, web attack detection, and suspicious activity detection in online/offline financial services. In this tutorial, we will present a comprehensive introduction of DLGAD from three key technical perspectives, including graph neural network (GNN) backbone, proxy task design, and graph anomaly measure. For each of these perspectives, we will review its inherent challenges, key intuitions, and underlying assumptions; objective functions, advantages, and disadvantages of state-of-the-art DLGAD methods will be discussed.