Quantum-based subgraph convolutional neural networks

PATTERN RECOGNITION

卷:88

Article

Zhang, Zhihong¹  Chen, Dongdong¹  Wang, Jianjia³  Bai, Lu²  Hancock, Edwin R.³ 

[1] Xiamen Univ, Xiamen, Peoples R China

[2] Cent Univ Finance & Econ, Beijing, Peoples R China

[3] Univ York, York, N Yorkshire, England

关键词: Graph convolutional neural networks;    Spatial construction;    Quantum walks;    Subgraph;   

DOI  :  10.1016/j.patcog.2018.11.002

来源: Elsevier

【 摘 要 】

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. (C) 2018 Elsevier Ltd. All rights reserved.

【 授权许可】

Free