One-class Temporal Graph Attention Neural Network for Dynamic Graph Anomaly Detection

Nowadays, the application of dynamic graphs in the modeling of complex systems has made a great achievement, which has aroused people's attention to anomaly detection of dynamic graphs. As an unsupervised learning task, anomaly detection is target at identifying the abnormal data that is different from the majority. One-class support vector machine, one of the classic anomaly detection algorithms, has been widely applied to find the outliers for it's stability, robustness and convenience. However, traditional anomaly detection algorithms always lose their effectiveness when applied to dynamic graph anomaly detection task. In order to solve the above problem, we design one-class temporal graph attention neural network (OCTGAT) for anomaly detection on dynamic graph. OCTGAT aims to integrate the powerful representation capabilities off temporal graph neural networks and the classical one-class objective. Compared with the given benchmarks, OGTGAT achieves significant improvements in the experiments.