Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs

26 Sep 2019Yecheng LyuXinming HuangZiming Zhang

Graph convolutional networks (GCNs) suffer from the irregularity of graphs, while more widely-used convolutional neural networks (CNNs) benefit from regular grids. To bridge the gap between GCN and CNN, in contrast to previous works on generalizing the basic operations in CNNs to graph data, in this paper we address the problem of how to project undirected graphs onto the grid in a {\em principled} way where CNNs can be used as backbone for geometric deep learning... (read more)

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