  Anomaly detection from graph data has drawn much attention due to its
practical significance in many critical applications including cybersecurity,
finance, and social networks. Existing data mining and machine learning methods
are either shallow methods that could not effectively capture the complex
interdependency of graph data or graph autoencoder methods that could not fully
exploit the contextual information as supervision signals for effective anomaly
detection. To overcome these challenges, in this paper, we propose a novel
method, Self-Supervised Learning for Graph Anomaly Detection (SL-GAD). Our
method constructs different contextual subgraphs (views) based on a target node
and employs two modules, generative attribute regression and multi-view
contrastive learning for anomaly detection. While the generative attribute
regression module allows us to capture the anomalies in the attribute space,
the multi-view contrastive learning module can exploit richer structure
information from multiple subgraphs, thus abling to capture the anomalies in
the structure space, mixing of structure, and attribute information. We conduct
extensive experiments on six benchmark datasets and the results demonstrate
that our method outperforms state-of-the-art methods by a large margin.
