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.

该研究提出了一种新的基于图的神经网络的多视图方法，它开发了一种系统和方法论的调整，针对非欧几里得流形的上下文进行了卷积、池化和多视图结构等经典深度学习方法的关键概念，提出了一种新的基于视图的卷积层和新的视图池化层，以综合从多个视图生成的信息，并在端到端的图神经网络架构中用于图分类，并展示了与其他最先进方法相竞争的结果。