Local and Global Contrastive Network for Unsupervised Graph Anomaly Detection

Unsupervised graph anomaly detection seeks to discover rare patterns in graph-structured data, enabling applications across diverse fields such as financial transactions, e-commerce, and beyond. Although recent methods based on graph autoencoder and graph contrastive consistency show considerable promise in this field, the former primarily addresses global anomalies, whereas the latter is more focused on local anomalies. This distinction limits their capacity to fully capture the abnormalities of nodes in the graph. Moreover, challenges such as overfitting of abnormal patterns and the introduction of unknown noise from random walk-based subgraph sampling remain prevalent. These issues contribute to suboptimal model performance. To tackle these challenges, we propose Local and Global Contrastive Networks (LGCN). LGCN first extracts graph features using a feature encoder module, and subsequently projects them into local and global feature subspaces. The global information learning module mitigates overfitting in reconstruction techniques to abnormal patterns by simultaneously accounting for both the similarities and differences between nodes. The local information learning module extracts anomaly information by evaluating the consistency between the center node and its corresponding context features. Extensive experiments across eight datasets demonstrate that LGCN outperforms existing methods in unsupervised graph anomaly detection.