Learning Vertex Convolutional Networks for Graph Classification

Understand

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.