Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection

Jiazhen Chen, Xiuqin Liang, Sichao Fu +2

Unsupervised graph anomaly detection (GAD) has received increasing attention in recent years, which aims to identify data anomalous patterns utilizing only unlabeled node informati…