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merit's Introduction

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}
}

merit's People

Contributors

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Stargazers

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Watchers

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Forkers

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merit's Issues

Questions about accuracy.

According to the implementation of other codes such as DGI, GRACE, etc., they all use the results of the last epoch or the minimum loss in the training process (early stopping) to calculate acc as the result. And your code is to calculate acc every 10 rounds, and use the best acc among all acc as your experimental result.

I think contrastive learning is unsupervised learning, and their codes are correct. It is unfair to compare the acc of your codes with them.

Why is the acc different from the MVGRL paper?

Hello! In your paper, your acc of MVGRL method on three datasets (Cora, CiteSeer, PubMed) are 82.9, 72.6, 79.4. But in MVGRL paper, the acc are 86.8, 73.3, 80.1. I think both of you use the same dataset, so could you please explain the reason?

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