https://github.com/tkipf/pygcn

Graph Convolutional Networks in PyTorch
https://github.com/tkipf/pygcn

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Graph Convolutional Networks in PyTorch

• Host: GitHub
• URL: https://github.com/tkipf/pygcn
• Owner: tkipf
• License: mit
• Created: 2017-10-26T19:05:04.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2020-09-20T18:24:56.000Z (about 4 years ago)
• Last Synced: 2024-10-01T15:02:29.741Z (about 1 month ago)
• Language: Python
• Size: 220 KB
• Stars: 5,157
• Watchers: 55
• Forks: 1,227
• Open Issues: 54
• Metadata Files:
  • Readme: README.md

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README

        
Graph Convolutional Networks in PyTorch

====

PyTorch implementation of Graph Convolutional Networks (GCNs) for semi-supervised classification [1].

For a high-level introduction to GCNs, see:

Thomas Kipf, [Graph Convolutional Networks](http://tkipf.github.io/graph-convolutional-networks/) (2016)

![Graph Convolutional Networks](figure.png)

Note: There are subtle differences between the TensorFlow implementation in https://github.com/tkipf/gcn and this PyTorch re-implementation. This re-implementation serves as a proof of concept and is not intended for reproduction of the results reported in [1].

This implementation makes use of the Cora dataset from [2].

## Installation

```python setup.py install```

## Requirements

  * PyTorch 0.4 or 0.5

  * Python 2.7 or 3.6

## Usage

```python train.py```

## References

[1] [Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016](https://arxiv.org/abs/1609.02907)

[2] [Sen et al., Collective Classification in Network Data, AI Magazine 2008](http://linqs.cs.umd.edu/projects/projects/lbc/)

## Cite

Please cite our paper if you use this code in your own work:

```

@article{kipf2016semi,

  title={Semi-Supervised Classification with Graph Convolutional Networks},

  author={Kipf, Thomas N and Welling, Max},

  journal={arXiv preprint arXiv:1609.02907},

  year={2016}

}

```