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157 changes: 88 additions & 69 deletions README.md
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<!-- # TGX -->
![TGX logo](imgs/2023_TGX_logo.png)

# Temporal Graph Analysis with TGX (WSDM 2024 Demo Track)
# 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://complexdata-mila.github.io/TGX/"><img src="https://img.shields.io/badge/docs-orange"></a>
</h4>

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
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.

### Data Loading ###
For detailed tutorial on how to load the datasets into `tgx.Graph`, see [`docs/tutorials/data_loader.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_loader.ipynb)

1. Load TGB datasets
```
import tgx
dataset = tgx.tgb_data("tgbl-wiki")
ctdg = tgx.Graph(dataset)
```
## Dependecies
TGX implementation works with `python >= 3.9` and can be installed as follows.

2. Load built-in datasets
```
dataset = tgx.builtin.uci()
ctdg = tgx.Graph(dataset)
```
1. Set up virtual environment (conda should work as well).
```
python -m venv ~/tgx_env/
source ~/tgx_env/bin/activate
```

3. Load custom datasets from `.csv`
```
from tgx.io.read import read_csv
toy_fname = "docs/tutorials/toy_data.csv"
edgelist = read_csv(toy_fname, header=True,index=False, t_col=0,)
tgx.Graph(edgelist=edgelist)
```
2. Install external packages
```
pip install -r requirements.txt
```

### Visualization and Statistics ###
For detailed tutorial on how 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)
3. Install local dependencies under root directory `/TGX`.
<!-- ```
pip install -e py-tgx
``` -->
```
pip install -e .
```

1. Discretize the network (required for viz)

```
dataset = tgx.builtin.uci()
ctdg = tgx.Graph(dataset)
time_scale = "weekly"
dtdg, ts_list = ctdg.discretize(time_scale=time_scale, store_unix=True)
```

2. Plot the number of nodes over time
4. [alternatively] Install from `test-pypi`.

```
tgx.degree_over_time(dtdg, network_name="uci")
```
```
pip install -i https://test.pypi.org/simple/ py-tgx
```
You can specify the version with `==`, note that the pypi version might not always be the most updated version

3. Compute novelty index
```
tgx.get_novelty(dtdg)
```

5. [optional] Install `mkdocs` dependencies to serve the documentation locally.
```
pip install mkdocs-glightbox
```

### Install dependency
Our 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. install external packages
```
pip install -r requirements.txt
```
## Data Loading
For detailed tutorial on how to load datasets as a `tgx.Graph`, see [`docs/tutorials/data_loader.ipynb`](https://github.com/ComplexData-MILA/TGX/blob/master/docs/tutorials/data_loader.ipynb).
Here are some simple examples on loading different datasets.

3. install local dependencies under root directory `/TGX`
<!-- ```
pip install -e py-tgx
``` -->
```
pip install -e .
```
1. Load TGB datasets.
```
import tgx
dataset = tgx.tgb_data("tgbl-wiki")
ctdg = tgx.Graph(dataset)
```

2. Load built-in datasets.
```
dataset = tgx.builtin.uci()
ctdg = tgx.Graph(dataset)
```

3. Load custom datasets from `.csv`.
```
from tgx.io.read import read_csv
toy_fname = "docs/tutorials/toy_data.csv"
edgelist = read_csv(toy_fname, header=True,index=False, t_col=0,)
tgx.Graph(edgelist=edgelist)
```

3. [alternatively] install from test-pypi
## Visualization and Statistics
For detailed tutorial on how 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)
Here are examples showing some of the TGX's functionalities.

```
pip install -i https://test.pypi.org/simple/ py-tgx
```
You can specify the version with `==`, note that the pypi version might not always be the most updated version
1. **Discretize** the network (required for some visualization).
```
dataset = tgx.builtin.uci()
ctdg = tgx.Graph(dataset)
time_scale = "weekly"
dtdg, ts_list = ctdg.discretize(time_scale=time_scale, store_unix=True)
```

2. Plot the **number of nodes over time**.

4. [optional] install mkdocs dependencies to serve the documentation locally
```
pip install mkdocs-glightbox
```
```
tgx.degree_over_time(dtdg, network_name="uci")
```

3. Compute **novelty** index.
```
tgx.get_novelty(dtdg)
```

### Creating new branch ###

<!--
### Creating new branch
first create the branch on github
```
git fetch origin
git checkout -b test origin/test
```
``` -->


### 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}
}
```
4 changes: 2 additions & 2 deletions docs/tutorials/data_loader.ipynb
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},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
"version": "3.9.6"
}
},
"nbformat": 4,
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70 changes: 63 additions & 7 deletions requirements.txt
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matplotlib>=3.8.0
pandas>=2.1.1
numpy>=1.26.0
seaborn>=0.13.0
tqdm>=4.66.1
scikit-learn>=1.3.1
py-tgb>=0.9.0
appdirs==1.4.4
args==0.1.0
certifi==2022.12.7
charset_normalizer==3.1.0
click==8.1.7
clint==0.5.1
contourpy==1.0.7
cycler==0.11.0
Cython==3.0.8
decorator==4.4.2
distlib==0.3.1
docker-pycreds==0.4.0
filelock==3.0.12
fonttools==4.41.0
geoopt==0.5.1
gitdb==4.0.11
GitPython==3.1.41
idna==3.4
importlib_resources==6.0.0
Jinja2==3.1.2
joblib==1.3.1
kiwisolver==1.4.4
MarkupSafe==2.1.2
matplotlib==3.7.0
more-itertools==8.7.0
mpmath==1.3.0
networkx==3.1
numpy==1.24.2
packaging==23.1
pandas==1.5.3
Pillow==9.5.0
protobuf==4.25.2
psutil==5.9.4
py-tgb==0.9.2
-e git+ssh://[email protected]/ComplexData-MILA/TGX.git@bf47a2946ef35c5f96ba8cbe9c3e397d39c5e58e#egg=py_tgx
pyparsing==3.0.9
python-dateutil==2.8.2
pytz==2023.3
PyYAML==6.0.1
requests==2.28.2
scikit_learn==1.3.0
scipy==1.10.1
seaborn==0.13.2
sentry-sdk==1.40.3
setproctitle==1.3.2
setuptools-scm==6.0.1
six==1.16.0
sklearn==0.0
smmap==5.0.1
sympy==1.12
threadpoolctl==3.2.0
torch==2.0.1
torch-geometric-temporal==0.54.0
torch_geometric==2.3.1
torch_scatter==2.1.1
torch_sparse==0.6.17
tqdm==4.65.0
typing_extensions==4.7.1
tzdata==2023.3
urllib3==1.26.15
virtualenv==20.0.18
wandb==0.16.3
zipp==3.15.0
33 changes: 23 additions & 10 deletions setup.py
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Expand Up @@ -8,16 +8,29 @@ def readme():

setup(
name="py-tgx",
version="0.2.2",
description="Temporal graph",
long_description=readme(),
long_description_content_type="text/markdown",
url="https://github.com/fpour/tgx",
author="Razieh Shirzadkhani",
author_email="[email protected]",
keywords="Temporal Graph visualization",
version="0.3.0",
description="Temporal Graph Visualization with TGX",
url="https://github.com/ComplexData-MILA/TGX",
keywords="Temporal Graph Visualization",
license="MIT",
packages=find_packages(),
install_requires=[],
include_package_data=True,
install_requires=[
'appdirs==1.4.4',
'networkx==3.1',
'numpy==1.24.2',
'pandas==1.5.3',
'py-tgb==0.9.2',
'requests==2.28.2',
'scikit_learn==1.3.0',
'scipy==1.10.1',
'seaborn==0.13.2',
'sklearn==0.0',
'torch==2.0.1',
'torch-geometric-temporal==0.54.0',
'torch_geometric==2.3.1',
'torch_scatter==2.1.1',
'torch_sparse==0.6.17',
'tqdm==4.65.0',
'wandb==0.16.3',
],
)

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