论文标题
用于图形分类的多视图自适应图卷积
Multi-view adaptive graph convolutions for graph classification
论文作者
论文摘要
在本文中，提出了一种针对基于图的神经网络的新型多视图方法。针对非欧几里得歧管的背景，开发了经典深度学习方法的关键概念的系统和方法学适应。拟议工作的目的是介绍一个新颖的多视图卷积层，以及一个新的视图汇总层，可使用：a）根据特征距离度量学习的特征距离矩阵的多个可训练的图表，该图形矩阵的特征矩阵，使用可训练的距离矩阵，将图形图形用于图形，并将多个图形用于图形，该图形是多个图形的，该图形是多个图形的，该图形是多个图形的，该图形均为图形，该图形是多个图形的，该图形是多个图形，该图形是多个图形，从多个生成的视图中综合信息。上述图层用于端到端图神经网络体系结构中，以进行图形分类，并向其他最新方法显示竞争结果。
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.
