Semi-supervised Graph Anomaly Detection via Multi-view Contrastive Learning

Graph anomaly detection aims to identify the abnormal nodes in a graph that deviate from the majority of nodes, which is critical in fields such as finance and social network analysis. Due to the unbearable costs of labeling anomalies, existing methods are mainly tackled from unsupervised or semi-supervised manners. Unfortunately, most proposed methods either lack prior knowledge of the anomalies or neglect the potential relation between labeled and unlabeled nodes, with limited improvement. In this paper, we propose a novel Semi-supervised learning framework for graph Anomaly Detection via Multi-view Contrastive Learning (SADMCL for abbreviation), which uses very few labels to promote detection performance. To be specific, we employ multi-view contrastive learning, i.e., sample-sample contrast, sample-instance contrast, and normal-anomaly contrast, to generate the representations with significant discrimination between normal and abnormal nodes, as well as the instances that respond to the normal features. By comparing the learned representations and the instances, we comprehensively calculate the anomaly score for each node and infer its label. Extensive experiments conducted on four real-world datasets demonstrate that our approach outperforms current state-of-the-art anomaly detection algorithms when provided with few labeled samples.