PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection
This work addresses efficiency issues in graph anomaly detection for domains like medicine and social networks, but it is incremental as it builds on existing reconstruction-based and contrastive learning methods.
The paper tackled the problem of node-level graph anomaly detection by introducing PREM, a method that simplifies the process to improve efficiency, achieving a 5% AUC improvement, 9x faster training, and reduced memory usage on the ACM dataset.
Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in various domains such as medicine, social networks, and e-commerce. However, challenges have arisen due to the diversity of anomalies and 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 PREprocessing and Matching (PREM for short). Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities. 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% improvement in AUC, a 9-fold increase in training speed, and sharply reduce memory usage compared to the most efficient baseline.