DualGAD: Dual-bootstrapped self-supervised learning for graph anomaly detection

Abstract

Graph anomaly detection (GAD) is an emerging and essential research field for discovering anomalous individuals (e.g., nodes or edges) that deviate significantly from the normal majority in an attributed graph. Unlike other anomaly types (e.g., image, text) with independence, it is not trivial to capture inherent and distinctive anomaly patterns as graph anomalies usually exist more complex relational interaction with a overwhelming amount of normal majority, which induces the inconsistency in feature and structure space. Recently, some studies have applied graph self-supervised learning on the GAD task and surge a new climax. Despite their success, the sub-optimal performance is only achieved because the imbalance nature of anomaly problem significantly dilutes anomalous information and causes the shortage of effective supervision signals. To address these challenges, we propose a dual-bootstrapped self-supervised approach, namely DualGAD, that consists of one generative module and one contrastive module. Specifically, we first sample a local subgraph for each target node and employ two mask strategies on subgraphs to break the short-range connection and reduce redundancy spread from majority neighbors. The generative module is equipped with the reconstruction objective to model the surrounding context of masked subgraphs, which learn discriminative representations by capturing the inconsistency patterns at both feature and structure space. Considering the local information is easily been over-emphasized, we elaborately tailor a novel cluster-guided contrastive learning module without relying on positive/negative pairs to learn the intrinsic anomaly property by aligning the perturbation distributions across views. Finally, the two self-supervised modules are seamlessly integrated and bootstrap each other for learning the discriminative representations to distinguish anomalies and normal majority. Extensive experiments demonstrate the superiority of DualGAD on the node-level anomaly detection task.