Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning

1 code implementation • 12 Sep 2023 • Jingcan Duan, Pei Zhang, Siwei Wang, Jingtao Hu, Hu Jin, Jiaxin Zhang, Haifang Zhou, Xinwang Liu

Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished.

Contrastive Learning Graph Anomaly Detection

UAVs Formation Flight under Multiple Obstacle and Communication Constraints

no code implementations • 12 Jan 2023 • Hu Jin

In order to improve the communication of the UAV network when the UAV formation in a complex military mission environment.

Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View

no code implementations • 1 Dec 2022 • Jingcan Duan, Siwei Wang, Pei Zhang, En Zhu, Jingtao Hu, Hu Jin, Yue Liu, Zhibin Dong

However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance.

Contrastive Learning Graph Anomaly Detection