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

Abstract

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.

Junjun Pan

PhD Student @ Griffith (04/2024-)

My research interests mainly focus on the areas of graph machine learning and trustworthy network, especially for the graph neural network and anomaly detection.

Yixin Liu

PhD Student @ Monash (01/2021-)

My research interests include machine learning, graph analysis and audio processing.

Yizhen Zheng

PhD Student @ Monash (07/2021-)

My research interests include machine learning and artificial intelligence.

Shirui Pan

Professor and ARC Future Fellow

My research interests include data mining, machine learning, and graph analysis.