  Anomaly detection on the attributed network has recently received increasing
attention in many research fields, such as cybernetic anomaly detection and
financial fraud detection. With the wide application of deep learning on graph
representations, existing approaches choose to apply euclidean graph encoders
as their backbone, which may lose important hierarchical information,
especially in complex networks. To tackle this problem, we propose an efficient
anomaly detection framework using hyperbolic self-supervised contrastive
learning. Specifically, we first conduct the data augmentation by performing
subgraph sampling. Then we utilize the hierarchical information in hyperbolic
space through exponential mapping and logarithmic mapping and obtain the
anomaly score by subtracting scores of the positive pairs from the negative
pairs via a discriminating process. Finally, extensive experiments on four
real-world datasets demonstrate that our approach performs superior over
representative baseline approaches.
