Community-Guided Contrastive Learning with Anomaly-Aware Reconstruction for Anomaly Detection on Attributed Networks

Anomaly detection on attributed networks is of wide practical application in many domains, such as business and cybersecurity. Typically, existing methods mainly focus on utilizing the graph neural networks (GNNs) that aggregate information from neighbors to learn node representations for detecting anomalies. However, it may ignore the information beyond the neighbors like community associations. Furthermore, roughly stacking multiple GNNs layers may lead to the over-smoothing problem, making nodes representations more similar and anomalies undistinguishable. In this paper, we propose a novel method, named CARD, to tackle these issues. Specifically, we propose different augmentation strategies to offer diverse scale information for CARD. Then, to better capture community associations, we establish a community-guided contrastive learning module that can capture different scale of structure information as well. To capture multiple attribute information and aid in anomaly detection, we design an anomaly-aware masked autoencoder, effectively making anomalies more distinguished. Extensive experiments on nine datasets show the superiority of CARD. Our code are available at https://github.com/scu-kdde/OAM-CARD-2024 .