A Quantum-Inspired Similarity Measure for the Analysis of Complete Weighted Graphs

Lu Bai, Luca Rossi, Lixin Cui, Jian Cheng, Edwin R. Hancock

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)


We develop a novel method for measuring the similarity between complete weighted graphs, which are probed by means of the discrete-time quantum walks. Directly probing complete graphs using discrete-time quantum walks is intractable due to the cost of simulating the quantum walk. We overcome this problem by extracting a commute time minimum spanning tree from the complete weighted graph. The spanning tree is probed by a discrete-time quantum walk which is initialized using a weighted version of the Perron-Frobenius operator. This naturally encapsulates the edge weight information for the spanning tree extracted from the original graph. For each pair of complete weighted graphs to be compared, we simulate a discrete-time quantum walk on each of the corresponding commute time minimum spanning trees and, then, compute the associated density matrices for the quantum walks. The probability of the walk visiting each edge of the spanning tree is given by the diagonal elements of the density matrices. The similarity between each pair of graphs is then computed using either: 1) the inner product or 2) the negative exponential of the Jensen-Shannon divergence between the probability distributions. We show that in both cases the resulting similarity measure is positive definite and, therefore, corresponds to a kernel on the graphs. We perform a series of experiments on publicly available graph datasets from a variety of different domains, together with time-varying financial networks extracted from data for the New York Stock Exchange. Our experiments demonstrate the effectiveness of the proposed similarity measures.

Original languageEnglish
Article number8759946
Pages (from-to)1264-1277
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number3
Publication statusPublished - Mar 2020
Externally publishedYes


  • Financial networks
  • graph kernels
  • graph similarity
  • Jensen-Shannon divergence
  • quantum walks

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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