<!-- # TGX -->
# Temporal Graph Analysis with TGX
<h4>
<a href="https://arxiv.org/abs/2402.03651"><img src="https://img.shields.io/badge/arXiv-pdf-yellowgreen"></a>
<a href="https://pypi.org/project/py-tgx/"><img src="https://img.shields.io/pypi/v/py-tgx.svg?color=brightgreen"></a>
<a href="https://complexdata-mila.github.io/TGX/"><img src="https://img.shields.io/badge/docs-orange"></a>
</h4>
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](https://tgb.complexdatalab.com/) and [Poursafaei et al. 2022](https://openreview.net/forum?id=1GVpwr2Tfdg) as well as any custom dataset in `.csv` format.
- TGX provides numerous temporal graph visualization plots and statistics out of the box.
## Dependecies
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`](https://pypi.org/project/py-tgx/):
```
pip install py-tgx
```
6. [optional] Install `mkdocs` dependencies to serve the documentation locally.
```
pip install mkdocs-glightbox
```
For tutorials on how to use TGX to generate visualizations and compute statistics for temporal graphs, see [`docs/tutorials/data_viz_stats.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_viz_stats.ipynb)
### Citation
If TGX is useful for your work, please consider citing it:
```bibtex
@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}
}
```
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"description": "<!-- # TGX -->\n\n\n# Temporal Graph Analysis with TGX\n<h4>\n\t<a href=\"https://arxiv.org/abs/2402.03651\"><img src=\"https://img.shields.io/badge/arXiv-pdf-yellowgreen\"></a>\n\t<a href=\"https://pypi.org/project/py-tgx/\"><img src=\"https://img.shields.io/pypi/v/py-tgx.svg?color=brightgreen\"></a>\n\t<a href=\"https://complexdata-mila.github.io/TGX/\"><img src=\"https://img.shields.io/badge/docs-orange\"></a>\n</h4> \n\nThis repository contains the code for the paper \"Temporal Graph Analysis with TGX\" (WSDM 2024, Demo Track).\n\nTGX overview:\n- TGX supports all datasets from [TGB](https://tgb.complexdatalab.com/) and [Poursafaei et al. 2022](https://openreview.net/forum?id=1GVpwr2Tfdg) as well as any custom dataset in `.csv` format. \n- TGX provides numerous temporal graph visualization plots and statistics out of the box.\n\n\n## Dependecies\nTGX implementation works with `python >= 3.9` and can be installed as follows.\n\n1. Set up virtual environment (conda should work as well).\n\t```\n\tpython -m venv tgx_env/\n\tsource tgx_env/bin/activate\n\t```\n\n2. Upgrade pip (Optional)\n\t```\n\tpip install --upgrade pip\n\t```\n\n3. Install external packages\n\t```\n\tpip install -r requirements.txt\n\t```\n\n4. Install local dependencies under root directory `/TGX`.\n\t```\n\tpip install -e .\n\t```\n\n5. [Aternative] Install TGX from [`PyPi`](https://pypi.org/project/py-tgx/):\n\n\t```\n\tpip install py-tgx\n\t```\n\n6. [optional] Install `mkdocs` dependencies to serve the documentation locally.\n\t```\n\tpip install mkdocs-glightbox\n\t```\n\n\nFor tutorials on how to use TGX to generate visualizations and compute statistics for temporal graphs, see [`docs/tutorials/data_viz_stats.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_viz_stats.ipynb)\n\n\n### Citation\nIf TGX is useful for your work, please consider citing it:\n```bibtex\n@article{shirzadkhani2024temporal,\n title={Temporal Graph Analysis with TGX},\n author={Shirzadkhani, Razieh and Huang, Shenyang and Kooshafar, Elahe and Rabbany, Reihaneh and Poursafaei, Farimah},\n journal={arXiv preprint arXiv:2402.03651},\n year={2024}\n}\n```\n",
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