|
--- |
|
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 |
|
``` --> |