Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/124786
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGray, C.-
dc.contributor.authorMitchell, L.-
dc.contributor.authorRoughan, M.-
dc.date.issued2019-
dc.identifier.citationJournal of Complex Networks, 2019; 7(6):896-912-
dc.identifier.issn2051-1310-
dc.identifier.issn2051-1329-
dc.identifier.urihttp://hdl.handle.net/2440/124786-
dc.descriptionAdvance Access Publication on 20 March 2019-
dc.description.abstractSampling random graphs is essential in many applications, and often algorithms use Markov chain Monte Carlo methods to sample uniformly from the space of graphs. However, often there is a need to sample graphs with some property that we are unable, or it is too inefficient, to sample using standard approaches. In this paper, we are interested in sampling graphs from a conditional ensemble of the underlying graph model. We present an algorithm to generate samples from an ensemble of connected random graphs using a Metropolis-Hastings framework. The algorithm extends to a general framework for sampling from a known distribution of graphs, conditioned on a desired property. We demonstrate the method to generate connected spatially embedded random graphs, specifically the well known Waxman network, and illustrate the convergence and practicalities of the algorithm.-
dc.description.statementofresponsibilityCaitlin Gray, Lewis Mitchell and Matthew Roughan-
dc.language.isoen-
dc.publisherOxford University Press (OUP)-
dc.rights© The authors 2019. Published by Oxford University Press. All rights reserved.-
dc.subjectrandom graphs; MCMC; network sampling; connected networks-
dc.titleGenerating connected random graphs-
dc.typeJournal article-
dc.identifier.doi10.1093/comnet/cnz011-
pubs.publication-statusPublished-
dc.identifier.orcidGray, C. [0000-0003-1928-7008]-
dc.identifier.orcidMitchell, L. [0000-0001-8191-1997]-
Appears in Collections:Aurora harvest 4
Mathematical Sciences publications

Files in This Item:
File Description SizeFormat 
hdl_124786.pdfAccepted version569.85 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.