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Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification.

Authors
  • Bai, Lu
  • Cui, Lixin
  • Jiao, Yuhang
  • Rossi, Luca
  • Hancock, Edwin R
Type
Published Article
Journal
IEEE transactions on pattern analysis and machine intelligence
Publication Date
Feb 01, 2022
Volume
44
Issue
2
Pages
783–798
Identifiers
DOI: 10.1109/TPAMI.2020.3011866
PMID: 32750832
Source
Medline
Language
English
License
Unknown

Abstract

In this paper, we develop a novel backtrackless aligned-spatial graph convolutional network (BASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized backtrackless aligned grid structures and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed BASGCN 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. Furthermore, the proposed BASGCN model can both adaptively discriminate the importance between specified vertices during the convolution process and reduce the notorious tottering problem of existing spatially-based GCNs related to the Weisfeiler-Lehman algorithm, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model.

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