  Nowadays, graph-structured data are increasingly used to model complex
systems. Meanwhile, detecting anomalies from graph has become a vital research
problem of pressing societal concerns. Anomaly detection is an unsupervised
learning task of identifying rare data that differ from the majority. As one of
the dominant anomaly detection algorithms, One Class Support Vector Machine has
been widely used to detect outliers. However, those traditional anomaly
detection methods lost their effectiveness in graph data. Since traditional
anomaly detection methods are stable, robust and easy to use, it is vitally
important to generalize them to graph data. In this work, we propose One Class
Graph Neural Network (OCGNN), a one-class classification framework for graph
anomaly detection. OCGNN is designed to combine the powerful representation
ability of Graph Neural Networks along with the classical one-class objective.
Compared with other baselines, OCGNN achieves significant improvements in
extensive experiments.
