Quantum-based subgraph convolutional neural networks

Zhihong Zhang, Dongdong Chen, Jianjia Wang, Lu Bai, Edwin R. Hancock

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)38 - 49
JournalPattern recognition
Early online date6 Nov 2018
Publication statusE-pub ahead of print - 6 Nov 2018

Bibliographical note

© 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.


  • Graph convolutional neural networks, Spatial construction, Quantum walks, Subgraph

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