PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection

graph anomaly detection (GAD) plays a critical role in identifying in various domains such as medicine, social networks, and e-commerce. However, challenges have arisen due to the dearth of labeled data. Existing methodologies - reconstruction-based and contrastive learning - while effective, often suffer from efficiency issues, stemming from their complex objectives and elaborate modules. To improve the efficiency of GAD, we introduce a simple method termed processing and atching (PREM). Our approach streamlines GAD, Comprising two modules - a pre-processing module and an ego-neighbor matching module - PREM eliminates the necessity for message-passing propagation during training, and employs a simple contrastive loss, leading to considerable reductions in training time and memory usage. Moreover, through rigorous evaluations of five real-world datasets, our method demonstrated robustness and effectiveness. Notably, when validated on the ACM dataset, PREM achieved a 5\