---
license: cc-by-nc-4.0
language:
- en
tags:
- code
pretty_name: README
---
<!-- # TGB -->
![TGB logo](logo.png)

**TGB 2.0** 


Overview of the Temporal Graph Benchmark (TGB) pipeline:
- TGB includes large-scale and realistic datasets from five different domains with both dynamic link prediction and node property prediction tasks.
- TGB automatically downloads datasets and processes them into `numpy`, `PyTorch` and `PyG compatible TemporalData` formats. 
- Novel TG models can be easily evaluated on TGB datasets via reproducible and realistic evaluation protocols. 
- TGB provides public and online leaderboards to track recent developments in temporal graph learning domain.



```
pip install py-tgb
```

### Links and Datasets

The project website can be found [here](https://tgb.complexdatalab.com/).

The API documentations can be found [here](https://shenyanghuang.github.io/TGB/).

all dataset download links can be found at [info.py](https://github.com/shenyangHuang/TGB/blob/main/tgb/utils/info.py)

TGB dataloader will also automatically download the dataset as well as the negative samples for the link property prediction datasets.

if website is unaccessible, please use [this link](https://tgb-website.pages.dev/) instead.


### Running Example Methods

- For the dynamic link property prediction task, see the [`examples/linkproppred`](https://github.com/shenyangHuang/TGB/tree/main/examples/linkproppred) folder for example scripts to run TGN, DyRep and EdgeBank on TGB datasets.
- For the dynamic node property prediction task, see the [`examples/nodeproppred`](https://github.com/shenyangHuang/TGB/tree/main/examples/nodeproppred) folder for example scripts to run TGN, DyRep and EdgeBank on TGB datasets.
- For all other baselines, please see the [TGB_Baselines](https://github.com/fpour/TGB_Baselines) repo.

### Acknowledgments
We thank the [OGB](https://ogb.stanford.edu/) team for their support throughout this project and sharing their website code for the construction of [TGB website](https://tgb.complexdatalab.com/).


### Citation

If code or data from this repo is useful for your project, please consider citing our paper:
```
@article{huang2023temporal,
  title={Temporal graph benchmark for machine learning on temporal graphs},
  author={Huang, Shenyang and Poursafaei, Farimah and Danovitch, Jacob and Fey, Matthias and Hu, Weihua and Rossi, Emanuele and Leskovec, Jure and Bronstein, Michael and Rabusseau, Guillaume and Rabbany, Reihaneh},
  journal={Advances in Neural Information Processing Systems},
  year={2023}
}
```
<!-- 

### 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 ~/tgb_env/
source ~/tgb_env/bin/activate
```

2. install external packages
```
pip install pandas==1.5.3
pip install matplotlib==3.7.1
pip install clint==0.5.1
```

install Pytorch and PyG dependencies (needed to run the examples)
```
pip install torch==2.0.0 --index-url https://download.pytorch.org/whl/cu117
pip install torch_geometric==2.3.0
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
```

3. install local dependencies under root directory `/TGB`
```
pip install -e .
```


### Instruction for tracking new documentation and running mkdocs locally

1. first run the mkdocs server locally in your terminal 
```
mkdocs serve
```

2. go to the local hosted web address similar to
```
[14:18:13] Browser connected: http://127.0.0.1:8000/
```

Example: to track documentation of a new hi.py file in tgb/edgeregression/hi.py


3. create docs/api/tgb.hi.md and add the following
```
# `tgb.edgeregression`

::: tgb.edgeregression.hi
```

4. edit mkdocs.yml 
```
nav:
  - Overview: index.md
  - About: about.md
  - API:
	other *.md files 
	- tgb.edgeregression: api/tgb.hi.md
```

### Creating new branch ###
```
git fetch origin

git checkout -b test origin/test
```

### dependencies for mkdocs (documentation)
```
pip install mkdocs
pip install mkdocs-material
pip install mkdocstrings-python
pip install mkdocs-jupyter
pip install notebook
```


### full dependency list
Our implementation works with python >= 3.9 and has the following dependencies
```
pytorch == 2.0.0
torch-geometric == 2.3.0
torch-scatter==2.1.1
torch-sparse==0.6.17
torch-spline-conv==1.2.2
pandas==1.5.3
clint==0.5.1
``` -->