Multi-view adaptive graph convolutions for graph classification

Nikolas Adaloglou, Nicholas Vretos, Petros Daras

Abstract:

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.