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arXiv · 1902.09936

Learning Vertex Convolutional Networks for Graph Classification

Lu Bai·Lixin Cui·Shu Wu·Yuhang Jiao·Edwin R. Hancock
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Abstract

In this paper, we develop a new aligned vertex convolutional network model to learn multi-scale local-level vertex features for graph classification. Our idea is to transform the graphs of arbitrary sizes into fixed-sized aligned vertex grid structures, and define a new vertex convolution operation by adopting a set of fixed-sized one-dimensional convolution filters on the grid structure. We show that the proposed model not only integrates the precise structural correspondence information between graphs but also minimises the loss of structural information residing on local-level vertices. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model.

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BibTeXRIS

Lu Bai, Lixin Cui, Shu Wu, Yuhang Jiao, Edwin R. Hancock. 2019-02-26. Learning Vertex Convolutional Networks for Graph Classification. https://arxiv.org/abs/1902.09936

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