abram07/merit

A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

MERIT

A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning.

Dependencies

• 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

Usage

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

Citation

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}
}