Graph anomaly detection is essential for many security-related fields but faces significant challenges in handling complex real-world graph data. Due to the complex and imbalanced graph structure, it is difficult to find abnormal points among many nodes. Current contrastive learning methods often overlook structural imperfections in real-world graphs, such as redundant edges and low-degree sparse nodes. Redundant connections may introduce noise during message passing, while sparse nodes receive insufficient structural information to accurately learn representation, which can degrade detection performance. To overcome above challenges, we propose SAA-GCL, an innovative framework that integrates adaptive structure adversarial augmentation with multi-view contrastive learning. Specifically, by edge weight learning and LMSE loss calculation, our approach adaptively optimizes the structure of the augmented graph, discards redundant edges as much as possible, and retains more discriminating features. For low-degree sparse nodes, we mix their self-networks with the self-networks of auxiliary nodes to improve the representation quality. In order to fully mine abnormal information, we use the multi-view contrastive loss function to distinguish positive and negative sample pairs within the view and maintain cross-view consistency. The framework adaptively refines the graph topology to suppress noisy edges and enhance representations for structurally weak nodes, so it can improve anomaly detection performance in the imbalanced structure attribute graph. Comprehensive experiments on six real-world graph datasets show that SAA-GCL is superior to existing methods in detection accuracy. Our code is open source at https://github.com/HZAI-ZJNU/SAAGCL.
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