A novel entropy-based graph signature from the average mixing matrix

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a novel entropic signature for graphs, where we probe the graphs by means of continuous-time quantum walks. More precisely, we characterise the structure of a graph through its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encapsulates the time-averaged behaviour of a continuous-time quantum walk on the graph, i.e., the ij-th element of the average mixing matrix represents the time-averaged transition probability of a continuous-time quantum walk from the vertex vi to the vertex vj. With this matrix to hand, we can associate a probability distribution with each vertex of the graph. We define a novel entropic signature by concatenating the average Shannon entropy of these probability distributions with their Jensen-Shannon divergence. We show that this new entropic measure can encaspulate the rich structural information of the graphs, thus allowing to discriminate between different structures. We explore the proposed entropic measure on several graph datasets abstracted from bioinformatics databases and we compare it with alternative entropic signatures in the literature. The experimental results demonstrate the effectiveness and efficiency of our method.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1339-1344
Number of pages6
ISBN (Electronic)9781509048472
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun Center, Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume0
ISSN (Print)1051-4651

Conference

Conference23rd International Conference on Pattern Recognition, ICPR 2016
Country/TerritoryMexico
CityCancun
Period4/12/168/12/16

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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