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.

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

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

make run