Learning Graph Convolutional Networks based on Quantum Vertex Information Propagation

Lu Bai, Yuhang Jiao, Lixin Cui, Luca Rossi, Yue Wang, Philip Yu, Edwin Hancock

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)

Abstract

This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. Unlike state-of-the-art Graph Convolutional Neural Network (GCNN) models, the proposed QSGCNN model incorporates the process of identifying transitive aligned vertices between graphs and transforms arbitrary sized graphs into fixed-sized aligned vertex grid structures. In order to learn representative graph characteristics, a new quantum spatial graph convolution is proposed and employed to extract multi-scale vertex features, in terms of quantum information propagation between grid vertices of each graph. Since the quantum spatial convolution preserves the grid structures of the input vertices (i.e., the convolution layer does not change the original spatial sequence of vertices), the proposed QSGCNN model allows to directly employ the traditional convolutional neural network architecture to further learn from the global graph topology, providing an end-to-end deep learning architecture that integrates the graph representation and learning in the quantum spatial graph convolution layer and the traditional convolutional layer for graph classifications. We demonstrate the effectiveness of the proposed QSGCNN model in relation to existing state-of-the-art methods. Experiments on benchmark graph classification datasets demonstrate the effectiveness of the proposed QSGCNN model.

Original languageEnglish
Article number9521820
JournalIEEE Transactions on Knowledge and Data Engineering
DOIs
Publication statusAccepted/In press - Aug 2021
Externally publishedYes

Keywords

  • Convolution
  • Convolutional neural networks
  • Data mining
  • Feature extraction
  • Graph Neural Networks
  • Kernel
  • Periodic structures
  • Quantum Graph Convolution
  • Quantum Propagation
  • Quantum Walks
  • Standards

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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