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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorVoitalov, Ivan-
dc.contributor.authorVan der Hoorn, Pim-
dc.contributor.authorKitsak, Maksim A.-
dc.contributor.authorPapadopoulos, Fragkiskos-
dc.contributor.authorKrioukov, Dmitri V.-
dc.date.accessioned2021-10-08T09:32:13Z-
dc.date.available2021-10-08T09:32:13Z-
dc.date.issued2020-12-
dc.identifier.citationPhysical Review Research, 2020, vol. 2, no. 4, articl. no. 043157en_US
dc.identifier.issn26431564-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/23207-
dc.description.abstractMaximum entropy null models of networks come in different flavors that depend on the type of constraints under which entropy is maximized. If the constraints are on degree sequences or distributions, we are dealing with configuration models. If the degree sequence is constrained exactly, the corresponding microcanonical ensemble of random graphs with a given degree sequence is the configuration model per se. If the degree sequence is constrained only on average, the corresponding grand-canonical ensemble of random graphs with a given expected degree sequence is the soft configuration model. If the degree sequence is not fixed at all but randomly drawn from a fixed distribution, the corresponding hypercanonical ensemble of random graphs with a given degree distribution is the hypersoft configuration model, a more adequate description of dynamic real-world networks in which degree sequences are never fixed but degree distributions often stay stable. Here, we introduce the hypersoft configuration model of weighted networks. The main contribution is a particular version of the model with power-law degree and strength distributions, and superlinear scaling of strengths with degrees, mimicking the properties of some real-world networks. As a byproduct, we generalize the notions of sparse graphons and their entropy to weighted networks.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofPhysical Review Researchen_US
dc.rights© The Author(s). Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectPhysicsen_US
dc.subjectPhysics and Societyen_US
dc.subjectStatistical Mechanicsen_US
dc.subjectNetworken_US
dc.subjectTopologyen_US
dc.subjectWeighted hypersoft configuration modelen_US
dc.subjectJoint distributionen_US
dc.subjectPower-law degree distributionen_US
dc.subjectSuperlinear scaling between strengths and degreesen_US
dc.subjectSparsityen_US
dc.titleWeighted hypersoft configuration modelen_US
dc.typeArticleen_US
dc.collaborationNortheastern Universityen_US
dc.collaborationEindhoven University of Technologyen_US
dc.collaborationDelft University of Technologyen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryPhysical Sciencesen_US
dc.journalsOpen Accessen_US
dc.countryUnited Statesen_US
dc.countryNetherlandsen_US
dc.countryCyprusen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1103/PhysRevResearch.2.043157en_US
dc.identifier.scopus2-s2.0-85101002808-
dc.identifier.urlhttp://arxiv.org/abs/2007.00124v2-
dc.relation.issue4en_US
dc.relation.volume2en_US
cut.common.academicyear2020-2021en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn2643-1564-
crisitem.journal.publisherAmerican Physical Society-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4072-5781-
crisitem.author.parentorgFaculty of Engineering and Technology-
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