Graph Anomaly Detection via Prototype-Aware Label Propagation (Student Abstract)

Abstract

Detecting anomalies on attributed graphs is a challenging task since labelled anomalies are highly labour-intensive by taking specialized domain knowledge to make anomalous samples not as available as normal ones. Moreover, graphs contain complex structure information as well as attribute information, leading to anomalies that can be typically hidden in the structure space, attribute space, and the mix of both. In this paper, we propose a novel model for graph anomaly detection named ProGAD. Specifically, ProGAD takes advance of label propagation to infer high-quality pseudo labels by considering the structure and attribute inconsistencies between normal and abnormal samples. Meanwhile, ProGAD introduces the prior knowledge of class distribution to correct and refine pseudo labels with a prototype-aware strategy. Experiments demonstrate that ProGAD achieves strong performance compared with the current state-of-the-art methods.