Rethinking graph anomaly detection: A self-supervised Group Discrimination paradigm with Structure-Aware

Junyi Yan, Enguang Zuo, Chen Chen, Cheng Chen, Jie Zhong, Tianle Li, Xiaoyi Lv

Abstract: Structural anomalies are the core problem in graph anomaly detection. However, the current mainstream self-supervised graph anomaly detection models do not directly model structural anomalies and their expensive time consumption limits the efficiency of graph anomaly detection. For this reason, we rethink graph anomaly detection and propose a self-supervised Group Discrimination paradigm with Structure-Aware (GDSA). Our model can be explicitly aware of the graph topology changes by multi-view structure disturbance. Moreover, GDSA transforms graph anomaly detection into discriminating the scalar summaries of positive and negative group nodes. The results of extensive experiments on four benchmark datasets show that GDSA outperforms current state-of-the-art methods, with the most significant AUC performance improvement of 28.7%. Notably, in scalability testing on a large-scale dataset, the training time and testing time of GDSA are 1181.0× and 5064.7× faster than the baseline, respectively, with 61.9% savings in memory usage.