Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation

Shuang Zhou, Xiao Huang, Ninghao Liu +3

Graph anomaly detection (GAD) is a vital task since even a few anomalies can pose huge threats to benign users. Recent semi-supervised GAD methods, which can effectively leverage t…