harishgovardhandamodar / tg-gan-continuous-time-temporal-graph-generation-with-deep-generative-models

TGGAN

This is the project for the following paper, accepted in proceeding for 30th The Web Conference 2021, Ljubljana, Slovenia. You can cite for now.

@article{zhang2020tg,
  title={TG-GAN: Deep Generative Models for Continuously-time Temporal Graph Generation},
  author={Zhang, Liming and Zhao, Liang and Qin, Shan and Pfoser, Dieter},
  journal={arXiv preprint arXiv:2005.08323},
  year={2020}
}

The main training and inference codes for different datasets are in main_*.py scripts.

There is also codes developed for dynamic graph metric in MMD distance evaluation. The continuous-time graph metrics are in evaluation.py use another library tacoma, and the folder continuous_time_evaluation_and_DSBM_matlab contains the discrete-time graph metrics and also DSBM models. The referred libraries can be found in the folder too. Please cite this paper properly if you need to use the evaluation codes.