Xiuqin Liang

Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection

Unsupervised graph anomaly detection (GAD) has received increasing attention in recent years. It aims to identify anomalous data patterns using only unlabeled node information from graph-structured data. However, prevailing unsupervised GAD methods typically assume complete node attributes and struc