This repository contains the code for the paper "Temporal Graph Analysis with TGX" (WSDM 2024, Demo Track).

TGX overview:

• TGX supports all datasets from TGB and Poursafaei et al. 2022 as well as any custom dataset in .csv format.
• TGX provides numerous temporal graph visualization plots and statistics out of the box.

TGX implementation works with python >= 3.9 and can be installed as follows.

1. Set up virtual environment (conda should work as well).

   python -m venv tgx_env/
   source tgx_env/bin/activate
   

2. Upgrade pip (Optional)

   pip install --upgrade pip
   

3. Install external packages

   pip install -r requirements.txt
   

4. Install local dependencies under root directory /TGX.

   pip install -e .
   

5. [Aternative] Install TGX from PyPi:

   pip install py-tgx
   

6. [optional] Install mkdocs dependencies to serve the documentation locally.

   pip install mkdocs mkdocs-material mkdocstrings-python mkdocs-glightbox mkdocs-jupyter ipython_genutils
   

For tutorials on how to use TGX to generate visualizations and compute statistics for temporal graphs, see docs/tutorials/data_viz_stats.ipynb

If TGX is useful for your work, please consider citing it:

@article{shirzadkhani2024temporal,
  title={Temporal Graph Analysis with TGX},
  author={Shirzadkhani, Razieh and Huang, Shenyang and Kooshafar, Elahe and Rabbany, Reihaneh and Poursafaei, Farimah},
  journal={arXiv preprint arXiv:2402.03651},
  year={2024}
}