Awesome
GRACE
<img src="grace.png" alt="model" style="zoom: 50%;" />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.
Usage
Train and evaluate the model by executing
python train.py --dataset Cora
The --dataset argument should be one of [ Cora, CiteSeer, PubMed, DBLP ].
Requirements
- 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.
Citation
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 = {http://arxiv.org/abs/2006.04131}
}