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

<h3> <a href="https://arxiv.org/abs/2307.01026"><img src="https://img.shields.io/badge/arXiv-pdf-yellowgreen"></a> <a href="https://pypi.org/project/py-tgb/"><img src="https://img.shields.io/pypi/v/py-tgb.svg?color=brightgreen"></a> <a href="https://tgb.complexdatalab.com/"><img src="https://img.shields.io/badge/website-blue"></a> <a href="https://docs.tgb.complexdatalab.com/"><img src="https://img.shields.io/badge/docs-orange"></a> </h3>

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs (preprint)

<h3> <a href="https://arxiv.org/abs/2406.09639v1"><img src="https://img.shields.io/badge/arXiv-pdf-yellowgreen"></a> </h3>

Overview of the Temporal Graph Benchmark (TGB) pipeline:

TGB dataloading and evaluation pipeline

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

See all version differences and update notes here

Announcements

Excited to announce TGB 2.0, expanding TGB to Temporal Knowledge Graphs and Temporal Heterogeneous Graphs

See our preprint here for details. Please install locally first. We welcome your feedback and suggestions.

Excited to announce TGX, a companion package for analyzing temporal graphs in WSDM 2024 Demo Track

TGX supports all TGB datasets and provides numerous temporal graph visualization plots and statistics out of the box. See our paper: Temporal Graph Analysis with TGX and TGX website.

Excited to announce 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 prompted 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

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}
}
<!-- ### 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 ``` -->