GraphGT: A Python repository from sailfish009

GraphGT: Machine Learning Datasets for Graph Generation and Transformation

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'.

Cite Us

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

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

Team

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

Contact

Send us an email or open an issue.

sailfish009/GraphGT