Dynamic Graph Anomaly Detection Model Combining Dual Behavior Contrast

Current dynamic graph anomaly detection models learn multibehavior patterns for abnormal edges poorly and rely too much on the differences in long-term snapshots. Aiming at the above problems, combine dual behavior contrast dynamic graph anomaly detection model is proposed. Firstly, a dual behavior learning module is designed, where the role-based behavior learning submodule constructs graphlet degree vector by identifying four self-isomorphic orbits to capture deep structural features, while the attribute-based behavior learning submodule obtains attribute vectors through graph convolutional network. Then, the results are combined in the dynamic edge representation module to form the dynamic representations of edges to capture dual behavior patterns. Lastly, the anomaly detection module is designed to detect newly generated edges by combining contrastive learning with gated recurrent unit. We conduct experiments from four perspectives: anomaly detection accuracy, parameter sensitivity, robustness of module variants, and model runtime efficiency. The results demonstrate that the model achieves a peak accuracy of 92.05% in the task of dynamic edge anomaly detection.