Abstract
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.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant no. 61602535, 61976235 and 61503422), the Open Projects Program of National Laboratory of Pattern Recognition (NLPR), the program for innovation research in Central University of Finance and Economics, and the Youth Talent Development Support Program by Central University of Finance and Economics, No. QYP1908. Corresponding Author: Lixin Cui, Email: cuilixin@cufe.edu.cn. The data/code will be available at https://github.com/baiuoy/ASGCN_ECML-PKDD2019.
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Bai, L., Jiao, Y., Cui, L., Hancock, E.R. (2020). Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11906. Springer, Cham. https://doi.org/10.1007/978-3-030-46150-8_28
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DOI: https://doi.org/10.1007/978-3-030-46150-8_28
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