  In this paper, a novel multi-view methodology for graph-based neural networks
is proposed. A systematic and methodological adaptation of the key concepts of
classical deep learning methods such as convolution, pooling and multi-view
architectures is developed for the context of non-Euclidean manifolds. The aim
of the proposed work is to present a novel multi-view graph convolution layer,
as well as a new view pooling layer making use of: a) a new hybrid Laplacian
that is adjusted based on feature distance metric learning, b) multiple
trainable representations of a feature matrix of a graph, using trainable
distance matrices, adapting the notion of views to graphs and c) a multi-view
graph aggregation scheme called graph view pooling, in order to synthesise
information from the multiple generated views. The aforementioned layers are
used in an end-to-end graph neural network architecture for graph
classification and show competitive results to other state-of-the-art methods.
