Keras-based implementation of graph convolutional networks for semi-supervised classification.

Thomas N. Kipf, Max Welling, Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017)

For a high-level explanation, have a look at our blog post:

Thomas Kipf, Graph Convolutional Networks (2016)

NOTE: This code is not intended to reproduce the experiments from the paper as the initialization scheme, dropout scheme, and dataset splits differ from the original implementation in TensorFlow: https://github.com/tkipf/gcn

python setup.py install

• keras (1.0.9 or higher)
• TensorFlow or Theano

python train.py

Sen et al., Collective Classification in Network Data, AI Magazine 2008

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

@inproceedings{kipf2017semi,
  title={Semi-Supervised Classification with Graph Convolutional Networks},
  author={Kipf, Thomas N. and Welling, Max},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2017}
}