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

Overview

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}
}
Owner
Graph Analysis & Deep Learning Laboratory, GRAND
GRaph ANalysis & Deep learning Laboratory (GRAND Lab) at Monash University
Graph Analysis & Deep Learning Laboratory, GRAND
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