OHATP: Graph Anomaly Detection with Orthogonal-Hyperspherical Augmentation and Topology Perception

Contrastive Learning (CL) has been widely used for Graph Anomaly Detection (GAD). However, existing augmentation techniques in such CL frameworks usually rely on random masking or structural alterations, which obliterates the irregularities that define anomalies and leads to *anomaly distortion*. Even improved methods using *multi-scale sampling* usually suffer from *blind contextual extraction*, which risks disrupting critical topological structures or introducing irrelevant information in the learned representations, thereby hardly capturing complex anomalies. In this paper, we propose Orthogonal-Hyperspherical Augmentation and Topology Perception (**OHATP**), a novel method that leverages the synergies at both the *feature* and *structure* levels to mitigate anomaly distortion and *blind sampling defects*. Specifically, during *feature-level* augmentation, we adapt projections and noise based on node *message-passing* influence to strengthen *feature diversity*, while utilizing projection orthogonality and hyperspherical noise constraints to preserve *feature discriminability*. To complement this feature-level augmentation, we design a *structure-level* topology perception module that leverages the high-quality representations learned from augmentation to screen abnormal dense substructures. Extensive experiments on benchmark datasets demonstrate that OHATP substantially outperforms state-of-the-art methods, achieving improvements of over 6% in AUROC and 29% in AUPRC. Code is available at [https://anonymous.4open.science/r/OHATP-83D7](https://anonymous.4open.science/r/OHATP-83D7).