Zhang, Zhihong, Chen, Dongdong, Wang, Jianjia et al. (2 more authors) (2018) Quantum-based subgraph convolutional neural networks. Pattern recognition. 38 - 49. ISSN 0031-3203
Abstract
This paper proposes a new graph convolutional neural network architecture based on a depth-based representation of graph structure deriving from quantum walks, which we refer to as the quantum-based subgraph convolutional neural network (QS-CNNs). This new architecture captures both the global topological structure and the local connectivity structure within a graph. Specifically, we commence by establishing a family of K-layer expansion subgraphs for each vertex of a graph by quantum walks, which captures the global topological arrangement information for substructures contained within a graph. We then design a set of fixed-size convolution filters over the subgraphs, which helps to characterise multi-scale patterns residing in the data. The idea is to apply convolution filters sliding over the entire set of subgraphs rooted at a vertex to extract the local features analogous to the standard convolution operation on grid data. Experiments on eight graph-structured datasets demonstrate that QS-CNNs architecture is capable of outperforming fourteen state-of-the-art methods for the tasks of node classification and graph classification.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier Ltd. All rights reserved.This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Graph convolutional neural networks, Spatial construction, Quantum walks, Subgraph |
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: | 26 Nov 2018 16:50 |
Last Modified: | 17 Oct 2024 08:39 |
Published Version: | https://doi.org/10.1016/j.patcog.2018.11.002 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1016/j.patcog.2018.11.002 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:139142 |