https://github.com/jiaqima/G3NN

A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)
https://github.com/jiaqima/G3NN

Last synced: 3 months ago
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A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

• Host: GitHub
• URL: https://github.com/jiaqima/G3NN
• Owner: jiaqima
• License: mit
• Created: 2019-09-13T06:34:28.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2021-11-14T05:45:27.000Z (almost 3 years ago)
• Last Synced: 2024-06-29T10:32:19.118Z (5 months ago)
• Language: Python
• Homepage: https://arxiv.org/abs/1905.10769
• Size: 12.7 KB
• Stars: 16
• Watchers: 4
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• awesome-graph-based-semi-supervised-learning - code

README

        
# G3NN

This repo provides a pytorch implementation for the 4 instantiations of the flexible generative framework as described in the following paper:

[A Flexible Generative Framework for Graph-based Semi-supervised Learning](https://arxiv.org/abs/1905.10769)

[Jiaqi Ma](https://www.jiaqima.com/)\*, [Weijing Tang](https://sites.google.com/umich.edu/weijingtang/home)\*, [Ji Zhu](http://dept.stat.lsa.umich.edu/~jizhu/), and [Qiaozhu Mei](http://www-personal.umich.edu/~qmei/). NeurIPS 2019.

(\*: equal contribution)

## Requirements

See `environment.yml`. Run `conda torch_env create -f environment.yml` to install the required packages.

## Run the code

Example: `python main.py --model lsm_gcn --dataset cora`

## Cite

```

@inproceedings{ma2019flexible,

  title={A Flexible Generative Framework for Graph-based Semi-supervised Learning},

  author={Ma, Jiaqi and Tang, Weijing and Zhu, Ji and Mei, Qiaozhu},

  booktitle={Advances in Neural Information Processing Systems},

  pages={3276--3285},

  year={2019}

}

```