Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/5384
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dc.contributorDepartment of Logistics and Maritime Studies-
dc.creatorShi, D-
dc.creatorZhou, H-
dc.creatorLiu, L-
dc.date.accessioned2014-12-11T08:28:51Z-
dc.date.available2014-12-11T08:28:51Z-
dc.identifier.issn1539-3755-
dc.identifier.urihttp://hdl.handle.net/10397/5384-
dc.language.isoenen_US
dc.publisherAmerican Physical Societyen_US
dc.rightsPhysical Review E © 2010 The American Physical Society. The Journal's web site is located at http://pre.aps.org/en_US
dc.subjectComplex networksen_US
dc.subjectIterative methodsen_US
dc.subjectMarkov processesen_US
dc.subjectNumerical analysisen_US
dc.titleMarkovian iterative method for degree distributions of growing networksen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage6-
dc.identifier.volume82-
dc.identifier.issue3-
dc.identifier.doi10.1103/PhysRevE.82.031105-
dcterms.abstractCurrently, simulation is usually used to estimate network degree distribution P(k) and to examine if a network model predicts a scale-free network when an analytical formula does not exist. An alternative Markovian chain-based numerical method was proposed by Shi et al. Phys. Rev. E 71 036140 (2005) to compute time-dependent degree distribution P(k,t). Although the numerical results demonstrate a quick convergence of P(k,t) to P(k) for the Barabási-Albert model, the crucial issue on the rate of convergence has not been addressed formally. In this paper, we propose a simpler Markovian iterative method to compute P(k,t) for a class of growing network models. We also provide an upper bound estimation of the error of using P(k,t) to represent P(k) for sufficiently large t, and we show that with the iterative method, the rate of convergence of P(k,t) is root linear.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationPhysical review. E, Statistical, nonlinear, and soft matter physics, Sept. 2010, v. 82, no. 3, 031105, p. 1-6-
dcterms.isPartOfPhysical review. E, Statistical, nonlinear, and soft matter physics-
dcterms.issued2010-09-
dc.identifier.isiWOS:000281488400001-
dc.identifier.scopus2-s2.0-77957192340-
dc.identifier.eissn1550-2376-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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