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Anomaly detection on the attributed network has recently received increasingattention in many research fields, such as cybernetic anomaly detection andfinancial fraud detection. With the wide application of deep learning on graphrepresentations, existing approaches choose to apply euclidean graph encodersas their backbone, which may lose important hierarchical information,especially in complex networks. To tackle this problem, we propose an efficientanomaly detection framework using hyperbolic self-supervised contrastivelearning. Specifically, we first conduct the data augmentation by performingsubgraph sampling. Then we utilize the hierarchical information in hyperbolicspace through exponential mapping and logarithmic mapping and obtain theanomaly score by subtracting scores of the positive pairs from the negativepairs via a discriminating process. Finally, extensive experiments on fourreal-world datasets demonstrate that our approach performs superior overrepresentative baseline approaches.

Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection