Overview

DIG: Dive into Graphs is a turnkey library for graph deep learning research.

DIG: A Turnkey Library for Diving into Graph Deep Learning Research by Meng Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Hao Yuan*, Shurui Gui, Zhao Xu, Haiyang Yu, Jingtun Zhang, Yi Liu, Keqiang Yan, Bora Oztekin, Haoran Liu, Xuan Zhang, Cong Fu, and Shuiwang Ji.

It includes unified implementations of data interfaces, common algorithms, and evaluation metrics for several advanced tasks. Our goal is to enable researchers to easily implement and benchmark algorithms. Currently, we consider the following research directions.

• Graph Generation
• Self-supervised Learning on Graphs
• Explainability of Graph Neural Networks
• Deep Learning on 3D Graphs

Usage

DIG could be large since it consists of multiple research directions. You are recommended to download the subdirectory of interest. For example, if you are interested in graph generation (ggraph):

svn export https://github.com/divelab/DIG/trunk/ggraph

We provide documentations on how to use our implemented data interfaces and evaluation metrics. For every algorithm, an instruction documentation is also available. You can get started with your interested directions.

• Graph Generation: ggraph
• Self-supervised Learning on Graphs: sslgraph
• Explainability of Graph Neural Networks: xgraph
• Deep Learning on 3D Graphs: 3dgraph

Contributing

We welcome any forms of contributions, such as reporting bugs and adding new algorithms. Please refer to our contributing guidelines for details.

Citing DIG

Please cite our paper if you find DIG useful in your work:

@article{liu2021dig,
      title={{DIG}: A Turnkey Library for Diving into Graph Deep Learning Research}, 
      author={Meng Liu and Youzhi Luo and Limei Wang and Yaochen Xie and Hao Yuan and Shurui Gui and Zhao Xu and Haiyang Yu and Jingtun Zhang and Yi Liu and Keqiang Yan and Bora Oztekin and Haoran Liu and Xuan Zhang and Cong Fu and Shuiwang Ji},
      journal={arXiv preprint arXiv:2103.12608},
      year={2021},
}

Contact

If you have any questions, please submit a new issue or contact us: Meng Liu [mengliu@tamu.edu] or Shuiwang Ji [sji@tamu.edu].