Robust Heterophilic Graph Learning against Label Noise for Anomaly Detection

Abstract

Given clean labels, Graph Neural Networks (GNNs) have shown promising abilities for graph anomaly detection. However, real-world graphs are inevitably noisy labeled, which drastically degrades the performance of GNNs. To alleviate it, some studies follow the local consistency (a.k.a homophily) assumption to conduct neighborhood-based label noise correction, and to dense raw graphs using raw features or representations learned by poisoned labels. But for the anomaly detection task, the graph is not always homophilic but more likely to be heterophilic, which would corrupt the above assumption due to complicating connection patterns and impairing the effects of message passing. To this end, we propose a novel label noise-resistant graph learning (NRGL) framework, which facilitates robust graph learning from the perspectives of structure augmentation and fine-grained label governance. Specifically, we first present an investigation to verify that increasing graph homophily could help resist label noise. Based on the observation, an unsupervised contrastive learning paradigm is then introduced so well that it cannot only adaptively extract the dual views from the raw graph as structure augmentation, but also enhance the robustness of node representations. Next, given robust node representations, the noisy labels are divided into three candidate sets based on the small-loss criterion for fine-grained noise governance. Furthermore, a node sampler is designed to take structure importance, class frequency, and confidence score into consideration, which helps select reliable and important nodes for training. Extensive experiments on real-world datasets demonstrate the effectiveness of our method.