Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection

Yuanchen Bei, Sheng Zhou, Jinke Shi +3

Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real…