Synthesizing global and local perspectives in contrastive learning for graph anomaly detection

Abstract

Graph data has shown explosive growth, with application scenarios covering social networks, e-commerce networks, financial transaction networks, etc. In this context, graph anomaly detection is particularly important, aiming to prevent various malicious activities. Existing approaches, however, are still limited in that they either ignore global information and focus only on aggregating neighbor information of the target node, or they utilize global context as a supervisory signal while ignoring local information. In certain scenarios, anomalies can only be detected in a single view (global or local). Furthermore, the issue of class imbalance in graph-based anomaly detection is exacerbated by the significant disparity between the number of benign user samples and anomalous samples in real-world scenarios. As a solution to the above challenges, we present a framework for synthesizing Global and Local perspectives in Contrastive Learning (GALCL). GALCL leverages multi-view contrast to integrate both global and local information. By using node-graph and node-subgraph cross-scale contrasts, the framework enhances the prominence of local and global information, thereby capturing anomaly information that might be missed by focusing solely on the global or local level. In addition, a class-wise loss function is adopted to alleviate class imbalances on the graph. Comprehensive experiments conducted on eight real-world datasets demonstrate that our method outperforms the current state-of-the-art methods.