Dual Contrastive Learning for Anomaly Detection in Attributed Networks

Abstract

Anomaly detection in attributed networks has been crucial in many critical domains and has gained significant attention in recent years. However, most existing methods fail to capture the complexity of anomalous patterns at different levels with suitable supervision signals. To address this issue, we propose a novel dual contrastive self-supervised learning method for attributed network anomaly detection. Specifically, our approach relies on two major components to determine the anomaly of nodes. The first component assesses self-consistency by determining whether a target node’s attributes are consistent with its contextual environment. The second component evaluates behavioral consistency by analyzing the relationships and interaction patterns between the target node and its one-hop neighbors, which determines if the behavior of these neighbors aligns with the expected pattern of the target node. Accordingly, our method designs two types of contrastive instance pairs to fully exploit the structural and attribute information for detecting anomalous nodes at different levels regarding two focused consistencies. This approach is more effective in detecting anomalies and mitigating the limitations of previous methods. We evaluated our method on six benchmark datasets, and the experimental results demonstrate the superiority of our methods against state-of-the-art methods.