Title: When Work Matters: Transforming Classical Network Structures to Graph CNN,
Abstract: Numerous pattern recognition applications can be formed as learning from
graph-structured data, including social network, protein-interaction network,
the world wide web data, knowledge graph, etc. While convolutional neural
network (CNN) facilitates great advances in gridded image/video understanding
tasks, very limited attention has been devoted to transform these successful
network structures (including Inception net, Residual net, Dense net, etc.) to
establish convolutional networks on graph, due to its irregularity and
complexity geometric topologies (unordered vertices, unfixed number of adjacent
edges/vertices). In this paper, we aim to give a comprehensive analysis of when
work matters by transforming different classical network structures to graph
CNN, particularly in the basic graph recognition problem. Specifically, we
firstly review the general graph CNN methods, especially in its spectral
filtering operation on the irregular graph data. We then introduce the basic
structures of ResNet, Inception and DenseNet into graph CNN and construct these
network structures on graph, named as G_ResNet, G_Inception, G_DenseNet. In
particular, it seeks to help graph CNNs by shedding light on how these
classical network structures work and providing guidelines for choosing
appropriate graph network frameworks. Finally, we comprehensively evaluate the
performance of these different network structures on several public graph
datasets (including social networks and bioinformatic datasets), and
demonstrate how different network structures work on graph CNN in the graph
recognition task.