LRAGAD: Local Information Recognition for Attribute Graph Anomaly Detection

Anomaly detection is a crucial technique for comprehending the intricate structures of data, and it has garnered significant attention in various real-life applications, including finance, transportation, and network security. Presently, existing anomaly detection methods primarily concentrate on shallow techniques like residual analysis and community discovery, as well as deep learning methods that employ self-encoders as the underlying framework. However, these current methods overlook the local information during training, resulting in subpar and unexplained results. To recognize the local information, in this paper, we propose a novel Local Information Recognition method for Attribute Graph Anomaly Detection (LRAGAD). Specifically, to use the contextual structural information, LRAGAD first constructs a contrastive learning representation by generating different substructures from the target nodes, while reconstructing the whole graph using self-encoders that can utilize the neighborhood information of target nodes. Moreover, to better understand the complex graph structure, LRAGAD uses anomaly score estimation for outlier prediction. Experimental results on five real-world datasets demonstrate that the proposed LRAGAD method obtains better performance on AUC scores.