This is the code for the Paper: deep GRAph Contrastive rEpresentation learning (GRACE).

For a thorough resource collection of self-supervised learning methods on graphs, you may refer to this awesome list.

Train and evaluate the model by executing

python train.py --dataset Cora

The --dataset argument should be one of [ Cora, CiteSeer, PubMed, DBLP ].

• torch 1.4.0
• torch-geometric 1.5.0
• sklearn 0.21.3
• numpy 1.18.1
• pyyaml 5.3.1

Install all dependencies using

pip install -r requirements.txt

If you encounter some problems during installing torch-geometric, please refer to the installation manual on its official website.

Please cite our paper if you use the code:

@inproceedings{Zhu:2020vf,
  author = {Zhu, Yanqiao and Xu, Yichen and Yu, Feng and Liu, Qiang and Wu, Shu and Wang, Liang},
  title = {{Deep Graph Contrastive Representation Learning}},
  booktitle = {ICML Workshop on Graph Representation Learning and Beyond},
  year = {2020},
  url = {https://arxiv.org/abs/2006.04131}
}