Convolutional Neural Networks are very efficient at processing signals defined on a discrete Euclidean space (such as images). However, as they can not be used on signals defined on an arbitrary graph, other models have emerged, aiming to extend its properties. We propose to review some of the major deep learning models designed to exploit the underlying graph structure of signals. We express them in a unified formalism, giving them a new and comparative reading.
@article{arxiv.1905.00496,
title = {A Unified Deep Learning Formalism For Processing Graph Signals},
author = {Myriam Bontonou and Carlos Lassance and Jean-Charles Vialatte and Vincent Gripon},
journal= {arXiv preprint arXiv:1905.00496},
year = {2019}
}