This repository is a PyTorch implementation of VGGM: Variational Graph Gaussian Mixture Model for Unsupervised Change Point Detection in Dynamic Networks (Published in IEEE Transactions on Information Forensics and Security)

Dependencies

• cuda = 11.3
• python >= 3.9
• pytorch >= 1.12.0
• numpy >= 1.24.3
• pyg >= 2.3.0
• scikit-learn >= 1.2.2
• scipy >= 1.16.0
• tqdm >= 4.46.1

Quick Start

• python main.py

Citation

@ARTICLE{10472540,
  author={Zhang, Xinxun and Jiao, Pengfei and Gao, Mengzhou and Li, Tianpeng and Wu, Yiming and Wu, Huaming and Zhao, Zhidong},
  journal={IEEE Transactions on Information Forensics and Security}, 
  title={VGGM: Variational Graph Gaussian Mixture Model for Unsupervised Change Point Detection in Dynamic Networks}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  keywords={Feature extraction;Vectors;Training;Task analysis;Security;Heuristic algorithms;Graph neural networks;Change point detection;Abnormal Events;Unsupervised learning;Graph neural networks;Dynamic networks},
  doi={10.1109/TIFS.2024.3377548}}