py-tgb

Temporal Graph Benchmark for Machine Learning on Temporal Graphs (NeurIPS 2023 Datasets and Benchmarks Track)

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.

To submit to TGB leaderboard, please fill in this google form

See all version differences and update notes here

Annoucements

Excited to annouce that TGB has been accepted to NeurIPS 2023 Datasets and Benchmarks Track!

Thanks to everyone for your help in improving TGB! we will continue to improve TGB based on your feedback and suggestions.

Please update to version 0.9.2

version 0.9.2

Update the fix for tgbl-flight where now the unix timestamps are provided directly in the dataset. If you had issues with tgbl-flight, please remove TGB/tgb/datasets/tgbl_flightand redownload the dataset for a clean install

version 0.9.1

Fixed an issue for tgbl-flight where the timestamp conversion is incorrect due to time zone differences. If you had issues with tgbl-flight before, please update your package.

version 0.9.0

Added the large tgbn-token dataset with 72 million edges to the nodeproppred dataset.

Fixed errors in tgbl-coin and tgbl-flight where a small set of edges are not sorted chronologically. Please update your dataset version for them to version 2 (will be promted in terminal).

Pip Install

You can install TGB via pip. Requires python >= 3.9

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/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 folder 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}
}