metadata
license: cc-by-nc-4.0
language:
- en
tags:
- code
pretty_name: README
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,PyTorchandPyG compatible TemporalDataformats. - 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.
The API documentations can be found here.
all dataset download links can be found at 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 instead.
Running Example Methods
- For the dynamic link property prediction task, see the
examples/linkproppredfolder for example scripts to run TGN, DyRep and EdgeBank on TGB datasets. - For the dynamic node property prediction task, see the
examples/nodeproppredfolder for example scripts to run TGN, DyRep and EdgeBank on TGB datasets. - For all other baselines, please see the TGB_Baselines repo.
Acknowledgments
We thank the OGB team for their support throughout this project and sharing their website code for the construction of TGB website.
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}
}