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Dataset Website | Paper

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

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'.

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}
}

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

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