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dc.contributor.authorMann, Peter Stephen
dc.contributor.authorSmith, V Anne
dc.contributor.authorMitchell, John B. O.
dc.contributor.authorDobson, Simon Andrew
dc.date.accessioned2021-02-04T16:30:14Z
dc.date.available2021-02-04T16:30:14Z
dc.date.issued2021-01-22
dc.identifier.citationMann , P S , Smith , V A , Mitchell , J B O & Dobson , S A 2021 , ' Random graphs with arbitrary clustering and their applications ' , Physical Review. E, Statistical, nonlinear, and soft matter physics , vol. 103 , no. 1 , 012309 . https://doi.org/10.1103/PhysRevE.103.012309en
dc.identifier.issn1539-3755
dc.identifier.otherPURE: 271506372
dc.identifier.otherPURE UUID: bce5e5b0-36b3-4eb3-9c3d-8f85876a236e
dc.identifier.otherORCID: /0000-0002-0379-6097/work/88267130
dc.identifier.otherORCID: /0000-0002-0487-2469/work/88267307
dc.identifier.otherORCID: /0000-0001-9633-2103/work/88268306
dc.identifier.otherScopus: 85099791332
dc.identifier.otherWOS: 000610780300004
dc.identifier.urihttp://hdl.handle.net/10023/21376
dc.description.abstractThe structure of many real networks is not locally treelike and, hence, network analysis fails to characterize their bond percolation properties. In a recent paper [P. Mann, V. A. Smith, J. B. O. Mitchell, and S. Dobson, arXiv:2006.06744], we developed analytical solutions to the percolation properties of random networks with homogeneous clustering (clusters whose nodes are degree equivalent). In this paper, we extend this model to investigate networks that contain clusters whose nodes are not degree equivalent, including multilayer networks. Through numerical examples, we show how this method can be used to investigate the properties of random complex networks with arbitrary clustering, extending the applicability of the configuration model and generating function formulation.
dc.format.extent10
dc.language.isoeng
dc.relation.ispartofPhysical Review. E, Statistical, nonlinear, and soft matter physicsen
dc.rightsCopyright © 2021 American Physical Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the final published version of the work, which was originally published at https://doi.org/10.1103/PhysRevE.103.012309.en
dc.subjectComplex networksen
dc.subjectRandom graphsen
dc.subjectClustered networksen
dc.subjectQA Mathematicsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQC Physicsen
dc.subjectT-NDASen
dc.subject.lccQAen
dc.subject.lccQA75en
dc.subject.lccQCen
dc.titleRandom graphs with arbitrary clustering and their applicationsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.Sir James Mackenzie Institute for Early Diagnosisen
dc.contributor.institutionUniversity of St Andrews.School of Chemistryen
dc.contributor.institutionUniversity of St Andrews.Office of the Principalen
dc.contributor.institutionUniversity of St Andrews.St Andrews Centre for Exoplanet Scienceen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.contributor.institutionUniversity of St Andrews.Centre for Higher Education Researchen
dc.contributor.institutionUniversity of St Andrews.Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews.Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.EaSTCHEMen
dc.contributor.institutionUniversity of St Andrews.Biomedical Sciences Research Complexen
dc.contributor.institutionUniversity of St Andrews.St Andrews Bioinformatics Uniten
dc.identifier.doihttps://doi.org/10.1103/PhysRevE.103.012309
dc.description.statusPeer revieweden
dc.identifier.urlhttp://arxiv.org/abs/2006.08427en


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