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dc.contributor.authorMann, Peter Stephen
dc.contributor.authorSmith, V.A.
dc.contributor.authorMitchell, John B. O.
dc.contributor.authorDobson, Simon Andrew
dc.date.accessioned2022-03-29T14:31:06Z
dc.date.available2022-03-29T14:31:06Z
dc.date.issued2022-04-30
dc.identifier278575493
dc.identifier90edc09f-3c75-4e57-9984-5caad87412f6
dc.identifier85128857791
dc.identifier000798356200007
dc.identifier.citationMann , P S , Smith , V A , Mitchell , J B O & Dobson , S A 2022 , ' Degree correlations in graphs with clique clustering ' , Physical Review. E, Statistical, nonlinear, and soft matter physics , vol. 105 , no. 4 , 044314 . https://doi.org/10.1103/PhysRevE.105.044314en
dc.identifier.issn1539-3755
dc.identifier.otherORCID: /0000-0002-0379-6097/work/111970981
dc.identifier.otherORCID: /0000-0002-0487-2469/work/111971951
dc.identifier.otherORCID: /0000-0001-9633-2103/work/111972182
dc.identifier.urihttps://hdl.handle.net/10023/25122
dc.descriptionFunding: This work was partially supported by the UK Engineering and Physical Sciences Research Council under grant number EP/N007565/1 (Science of Sensor Systems Software).en
dc.description.abstractCorrelations among the degrees of nodes in random graphs often occur when clustering is present. In this paper we define a joint-degree correlation function for nodes in the giant component of clustered configuration model networks which are comprised of higher-order subgraphs. We use this model to investigate, in detail, the organisation among nearest-neighbour subgraphs for random graphs as a function of subgraph topology as well as clustering. We find an expression for the average joint degree of a neighbour in the giant component at the critical point for these networks. Finally, we introduce a novel edge-disjoint clique decomposition algorithm and investigate the correlations between the subgraphs of empirical networks.
dc.format.extent14
dc.format.extent1027228
dc.language.isoeng
dc.relation.ispartofPhysical Review. E, Statistical, nonlinear, and soft matter physicsen
dc.subjectComplex networksen
dc.subjectClusteringen
dc.subjectQA Mathematicsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQC Physicsen
dc.subjectT-NDASen
dc.subjectMCCen
dc.subject.lccQAen
dc.subject.lccQA75en
dc.subject.lccQCen
dc.titleDegree correlations in graphs with clique clusteringen
dc.typeJournal articleen
dc.contributor.sponsorEPSRCen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. St Andrews Bioinformatics Uniten
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. 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. School of Chemistryen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.identifier.doi10.1103/PhysRevE.105.044314
dc.description.statusPeer revieweden
dc.identifier.grantnumberEP/N007565/1en


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