Anomaly Subgraph Detection through High-Order Sampling Contrastive Learning

Anomaly subgraph detection is a crucial task in various real-world applications, including identifying high-risk areas, detecting river pollution, and monitoring disease outbreaks. Early traditional graph-based methods can obtain high-precision detection results in scenes with small-scale graphs and obvious anomaly features. Most existing anomaly detection methods based on deep learning primarily concentrate on identifying anomalies at the node level, while neglecting to detect anomaly groups in the internal structure. In this paper, we propose a novel end-to-end Graph Neural Network (GNN) based anomaly subgraph detection approach(ASD-HC) in graph-structured data. 1)We propose a high-order neighborhood sampling strategy to construct our node and k-order neighbor-subgraph instance pairs. 2)Anomaly features of nodes are captured through a self-supervised contrastive learning model. 3) Detecting the maximum connected anomaly subgraph is performed by integrating the Non-parameter Graph Scan statistics and a Random Walk module. We evaluate ASD-HC against five state-of-the-art baselines using five benchmark datasets. ASD-HC outperforms the baselines by over 13.01% in AUC score. Various experiments demonstrate that our approach effectively detects anomaly subgraphs within large-scale graphs.