  This paper deals with the unification of local and non-local signal
processing on graphs within a single convolutional neural network (CNN)
framework. Building upon recent works on graph CNNs, we propose to use
convolutional layers that take as inputs two variables, a signal and a graph,
allowing the network to adapt to changes in the graph structure. In this
article, we explain how this framework allows us to design a novel method to
perform style transfer.
