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Numerous pattern recognition applications can be formed as learning fromgraph-structured data, including social network, protein-interaction network,the world wide web data, knowledge graph, etc. While convolutional neuralnetwork (CNN) facilitates great advances in gridded image/video understandingtasks, very limited attention has been devoted to transform these successfulnetwork structures (including Inception net, Residual net, Dense net, etc.) toestablish convolutional networks on graph, due to its irregularity andcomplexity geometric topologies (unordered vertices, unfixed number of adjacentedges/vertices). In this paper, we aim to give a comprehensive analysis of whenwork matters by transforming different classical network structures to graphCNN, particularly in the basic graph recognition problem. Specifically, wefirstly review the general graph CNN methods, especially in its spectralfiltering operation on the irregular graph data. We then introduce the basicstructures of ResNet, Inception and DenseNet into graph CNN and construct thesenetwork structures on graph, named as G_ResNet, G_Inception, G_DenseNet. Inparticular, it seeks to help graph CNNs by shedding light on how theseclassical network structures work and providing guidelines for choosingappropriate graph network frameworks. Finally, we comprehensively evaluate theperformance of these different network structures on several public graphdatasets (including social networks and bioinformatic datasets), anddemonstrate how different network structures work on graph CNN in the graphrecognition task.

When Work Matters: Transforming Classical Network Structures to Graph CNN