Graph Anomaly Detection via Diffusion Enhanced Multi-View Contrastive Learning

Abstract

Graph Anomaly Detection (GAD) is of critical importance in areas such as cybersecurity, finance, and healthcare. Detecting anomalous nodes in graph data is a challenging task due to intricate interactions and attribute inconsistencies. Existing methods often distinguish anomalous nodes by using contrasting strategies at various scales. However, they overlook the enhancement methods of positive and negative sample pairs in the contrastive learning process, which can have a significant impact on the robustness and accuracy of the model. To address these limitations, we propose an innovative contrastive self-supervised approach called Diffusion Enhanced Multi-View Contrastive Learning (DE-GAD), which jointly optimizes a diffusion-based enhancement module and a multi-view contrastive learning-based module to better identify anomalous information. Specifically, in the diffusion-based enhancement module, we use the noise addition and stepwise denoising outputs of the diffusion model to enhance the original graphs, and use the loss of reconstruction to the original graphs as one of the criteria for anomaly detection. Second, in the multi-view contrastive module, we establish three contrastive views, namely node–node contrast, node–subgraph contrast, and subgraph–subgraph contrast, to enable the model to better capture the underlying relationships of graph nodes and thereby identify anomalies in the structural space. Finally, two complementary modules and their corresponding losses are integrated to detect anomalous nodes more accurately. Empirical experiments conducted on six benchmark datasets demonstrate the superiority of our proposed approach over existing methods.