keras-gcn

Deep Learning on Graphs with Keras

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

Installation

python setup.py install

Dependencies

• keras (1.0.9 or higher)
• TensorFlow or Theano

Usage

python train.py

Dataset reference (Cora)

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

Cite

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}
}