Bai, Lu, Jiao, Yuhang, Cui, Lixin et al. (1 more author) (2020) Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification. In: Brefeld, Ulf, Fromont, Elisa, Hotho, Andreas, Knobbe, Arno, Maathuis, Marloes and Robardet, Céline, (eds.) Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science . Springer , Cham , pp. 464-482.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2020. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 07 May 2020 08:00 |
Last Modified: | 16 Oct 2024 11:08 |
Published Version: | https://doi.org/10.1007/978-3-030-46150-8_28 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Identification Number: | 10.1007/978-3-030-46150-8_28 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160407 |