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dc.contributor.authorPérez Rosés, Hebert
dc.contributor.authorSebé Feixas, Francesc
dc.date.accessioned2015-02-19T11:48:26Z
dc.date.issued2014
dc.identifier.issn1747-7778
dc.identifier.urihttp://hdl.handle.net/10459.1/47997
dc.description.abstractIn many simulation studies involving networks there is the need to rely on a sample network to perform the simulation experiments. In many cases, real network data is not available due to privacy concerns. In that case we can recourse to synthetic data sets with similar properties to the real data. In this paper we discuss the problem of generating synthetic data sets for a certain kind of online social network, for simulation purposes. Some popular online social networks, such as LinkedIn and ResearchGate, allow user endorsements for speci fi c skills. For each particular skill, the endorsements give rise to a directed subgraph of the corresponding network, where the nodes correspond to network members or users, and the arcs represent endorsement relations. Modelling these endorsement digraphs can be done by formulating an optimization problem, which is amenable to different heuristics. Our con- struction method consists of two stages: The fi rst one simulates the growth of the network, and the second one solves the afore- mentioned optimization problem to construct the endorsements.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherPalgrave Macmillan
dc.relation.isformatofVersió postprint del document publicat a https://doi.org/10.1057/jos.2014.29
dc.relation.ispartofJournal of Simulation, 2015, vol. 9, núm. 4, p. 279–286
dc.rights(c) Operational Research Society, 2014
dc.subjectHeuristics
dc.subjectNetworks and graphs
dc.subjectOptimization
dc.subjectSimulation
dc.subject.classificationHeurística
dc.subject.classificationXarxes d'ordinadors
dc.subject.otherHeuristic
dc.subject.otherComputer networks
dc.titleSynthetic generation of social network data with endorsements
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2015-02-19T11:48:26Z
dc.identifier.idgrec022222
dc.type.versionacceptedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.1057/jos.2014.29


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