Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection
This addresses anomaly detection in domains like cybersecurity and finance, offering an incremental improvement by incorporating hyperbolic geometry into existing contrastive learning methods.
The paper tackles the problem of anomaly detection in attributed networks by proposing a hyperbolic self-supervised contrastive learning framework that preserves hierarchical information, demonstrating superior performance over baselines on four real-world datasets.
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.