GraphGT: Machine Learning Datasets for Graph Generation and Transformation (NeurIPS 2021)

Dataset Website | Paper

Anouncement As the data downloading through pip-package is problematic, especially for large files, we encourage you to download the dataset by the downloadable link (clicking the name of the dataset) provided here.

Installation

Using pip

To install the core environment dependencies of GraphGT, use pip:

pip install GraphGT

Note: GraphGT is in the beta release. Please update your local copy regularly by

pip install GraphGT --upgrade

DataLoader

import graphgt 
dataloader = graphgt.DataLoader(name=KEY, save_path='./', format='numpy')

KEY: 'qm9', 'zinc', 'moses', 'chembl', 'profold', 'kinetics', 'ntu', 'collab', 'n_body_charged', 'n_body_spring', 'random_geometry', 'waxman', 'traffic_bay', 'traffic_la', 'scale_free_{10|20|50|100}', 'ER_{20|40|60}', 'IoT_{20|40|60}', 'authen'.

All the datasets could be downloaded by the link in our website, while we are working hard to make sure all of them are downloadable through pip.

Cite Us

If you use our dataset in your work, please cite us:

@inproceedings{du2021graphgt,
  title={GraphGT: Machine Learning Datasets for Graph Generation and Transformation},
  author={Du, Yuanqi and Wang, Shiyu and Guo, Xiaojie and Cao, Hengning and Hu, Shujie and Jiang, Junji and Varala, Aishwarya and Angirekula, Abhinav and Zhao, Liang},
  booktitle={NeurIPS 2021},
  year={2021}
}

Team

Yuanqi Du (Leader), Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao (Advisor)

Issue

Please raise a GitHub issue if you have any question.

Contact

Send us an email or open an issue.