A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning.
- Python (>=3.6)
- PyTorch (>=1.7.1)
- NumPy (>=1.19.2)
- Scikit-Learn (>=0.24.1)
- Scipy (>=1.6.1)
- Networkx (>=2.5)
To install all dependencies:
pip install -r requirements.txt
Here we provide the implementation of MERIT along with Cora and Citeseer dataset.
To train and evaluate on Cora:
python run_cora.py
To train and evaluate on Citeseer:
python run_citeseer.py
If you use our code in your research, please cite the following article:
@inproceedings{Jin2021MultiScaleCS,
title={Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning},
author={Ming Jin and Yizhen Zheng and Yuan-Fang Li and Chen Gong and Chuan Zhou and Shirui Pan},
booktitle={The 30th International Joint Conference on Artificial Intelligence (IJCAI)}
year={2021}
url={https://arxiv.org/abs/2105.05682}
}