  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.

Machine Learning;Machine Learning