Papers › When Work Matters: Transforming Classical Network Structures to Graph CNN

When Work Matters: Transforming Classical Network Structures to Graph CNN

7 Jul 2018arXiv:1807.02653archive 2025-07-28

Wenting Zhao, Chunyan Xu, Zhen Cui, Tong Zhang, Jiatao Jiang, Zhen-Yu Zhang, Jian Yang

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.

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Tasks

Graph Classification Video Understanding

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Graph Classification	COLLAB	G_DenseNet	Accuracy	83.16%	#4 of 39	Archive leaderboard	report
Graph Classification	ENZYMES	G_Inception	Accuracy	67.50%	#17 of 54	Archive leaderboard	report
Graph Classification	IMDb-B	G_ResNet	Accuracy	79.90%	#8 of 51	Archive leaderboard	report
Graph Classification	IMDb-M	G_ResNet	Accuracy	54.53%	#6 of 36	Archive leaderboard	report
Graph Classification	MUTAG	G_Inception	Accuracy	95.00%	#5 of 74	Archive leaderboard	report
Graph Classification	NCI109	G_DenseNet	Accuracy	80.66	#22 of 38	Archive leaderboard	report
Graph Classification	PTC	G_DenseNet	Accuracy	73.24%	#7 of 37	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Global Average Pooling Kaiming Initialization Max Pooling ReLU Residual Block Residual Connection Softmax

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