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 (2016)

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}
}