/cnn_graph

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, https://arxiv.org/abs/1606.09375

Primary LanguageJupyter NotebookMIT LicenseMIT

Spectral Graph Convolutional Neural Network (SGCNN)

The code in this repository implements an efficient generalization of the popular Convolutional Neural Networks (CNNs) to arbitrary graphs, presented in our paper:

Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, Neural Information Processing Systems (NIPS), 2016.

  • Please cite the above paper if you use our code.
  • The code is released under the terms of the MIT license.

Installation

  1. Clone this repository.

    git clone https://github.com/mdeff/cnn_graph
cd cnn_graph

  2. Install the dependencies. Please edit requirements.txt to choose the TensorFlow version (CPU / GPU, Linux / Mac) you want to install, or install it beforehand. The code was developed with TF 0.8.

    pip install -r requirements.txt  # or make install

  3. Play with the Jupyter notebooks.

    jupyter notebook

Reproducing our results

Run all the notebooks to reproduce the experiments presented in the paper.

make run