Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

Lu Bai, Yuhang Jiao, Lixin Cui, Edwin R. Hancock

Research output: Working paperPreprint


In this paper, we develop a novel Aligned-Spatial Graph Convolutional Network (ASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized aligned grid structures, and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed ASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatially-based Graph Convolutional Network (GCN) models, but also bridges the theoretical gap between traditional Convolutional Neural Network (CNN) models and spatially-based GCN models. Moreover, the proposed ASGCN model can adaptively discriminate the importance between specified vertices during the process of spatial graph convolution, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model.
Original languageUndefined/Unknown
Publication statusPublished - 6 Apr 2019

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Bibliographical note

arXiv admin note: text overlap with arXiv:1902.09936, arXiv:1809.01090


  • cs.LG
  • stat.ML

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