https://github.com/trustagi-lab/merit
[IJCAI 2021] A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning".
https://github.com/trustagi-lab/merit
graph-neural-networks graph-representation-learning graph-self-supervised-learning self-supervised-distillation
Last synced: about 1 year ago
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[IJCAI 2021] A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning".
- Host: GitHub
- URL: https://github.com/trustagi-lab/merit
- Owner: TrustAGI-Lab
- License: mit
- Created: 2021-01-22T14:15:56.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2022-10-13T11:10:18.000Z (over 3 years ago)
- Last Synced: 2025-03-27T14:55:28.858Z (over 1 year ago)
- Topics: graph-neural-networks, graph-representation-learning, graph-self-supervised-learning, self-supervised-distillation
- Language: Python
- Homepage:
- Size: 31.2 MB
- Stars: 40
- Watchers: 1
- Forks: 8
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MERIT
A PyTorch implementation of our IJCAI-21 paper [Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning](https://arxiv.org/abs/2105.05682).
## 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}
}
```