This is the official pytorch implementation of Paper 'Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis', accepted by The 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024.

Setup

Enviroments

• Python (Jupyter notebook)

Python requirements

• python=3.8.715
• cudatoolkit=11.6
• pytorch=1.12.1+cu116
• numpy=1.23.4
• matplotlib=3.6.0
• scipy=1.9.3
• networkx=2.8.7
• gudhi=3.6.0

Datasets

• Datasets with ground truth labels are all available from "Data" folder
• Synthetic data are generated in the Jupyter notebook script
• Datasets source and processing code:
  • Enron: https://doi.org/10.1371/journal.pone.0195993
  • Highschool: https://doi.org/10.1371/journal.pone.0195993
  • DBLP: https://github.com/houchengbin/GloDyNE
  • Cora: https://github.com/houchengbin/GloDyNE
  • DBLPdyn: We edit data processing code of generating DBLP dataset. The code and raw data are given in "Data/dblp_dyn" folder.

Run

• Folder "Experiments": contains all code (Python / Jupyter Notebook) for producing the results in the experiments
• Training procedure was performed with a NVIDIA 3090 GPU on PyTorch platfom.

Citation

if you find our work useful in your research, please consider citing:

@article{kong2023toporeg, 
   author = {Dexu Kong and Anping Zhang and Yang Li}, 
   title = {Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis}, 
   journal={AAAI 2024},
   year = {2023}, 
}