Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
The code in this repository implements an efficient generalization of the
popular Convolutional Neural Networks (CNNs) to arbitrary graphs, presented in
our paper:
Install the dependencies. The code should run with TensorFlow 1.0 and newer.
pip install -r requirements.txt # or make install
Play with the Jupyter notebooks.
jupyter notebook
Reproducing our results
Run all the notebooks to reproduce the experiments onMNIST and 20NEWS presented in
the paper.
cd nips2016
make
Using the model
To use our graph ConvNet on your data, you need:
a data matrix where each row is a sample and each column is a feature,
a target vector,
optionally, an adjacency matrix which encodes the structure as a graph.
See the usage notebook for a simple example with fabricated data.
Please get in touch if you are unsure about applying the model to a different
setting.
License & co
The code in this repository is released under the terms of the MIT license.
Please cite our paper if you use it.
@inproceedings{cnn_graph,
title = {Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering},
author = {Defferrard, Micha"el and Bresson, Xavier and Vandergheynst, Pierre},
booktitle = {Advances in Neural Information Processing Systems},
year = {2016},
url = {https://arxiv.org/abs/1606.09375},
}