We present a TensorFlow implementation of the Graph Convolutional Neural Network illustrated in:

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
IEEE Transactions on Signal Processing, 2018
Ron Levie*, Federico Monti*, Xavier Bresson, Michael M. Bronstein

https://arxiv.org/abs/1705.07664

The repository contains a sparse implementation of the NN used for solving the MNIST digits classification problem described in the paper. Rational spectral filters are approximated with Jacobi Method to provide an efficient solution.

CayleyNet is a Graph CNN with spectral zoom properties able to effectively operate with signals defined over graphs. Thanks to its particular spectral properties, CayleyNet is well suited for dealing with a variety of different domains (e.g. citation networks, community graphs, user/item similarity graphs...). Variations of the architecture here implemented achieved state-of-the-art performance on vertex classification, community detection and matrix completion tasks.

inf.usi.ch/phd/monti
geometricdeeplearning.com