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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/49833
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dc.creatorTiago Alves Schieber de Jesuspt_BR
dc.creatorLaura Carpipt_BR
dc.creatorAlbert Dıaz-Guilerapt_BR
dc.creatorPanos M. Pardalospt_BR
dc.creatorCristina Masollerpt_BR
dc.creatorMartın G. Ravettipt_BR
dc.date.accessioned2023-02-09T17:32:29Z-
dc.date.available2023-02-09T17:32:29Z-
dc.date.issued2017-
dc.identifier.doi10.1038/ncomms13928pt_BR
dc.identifier.issn20411723pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/49833-
dc.description.resumoIdentifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.pt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentFCE - DEPARTAMENTO DE CIÊNCIAS ADMINISTRATIVASpt_BR
dc.publisher.departmentICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃOpt_BR
dc.publisher.initialsUFMGpt_BR
dc.rightsAcesso Abertopt_BR
dc.subject.otherAdministração de empresaspt_BR
dc.titleQuantification of network structural dissimilaritiespt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttp://www.nature.com/articles/ncomms13928doi:10.1038/ncomms13928pt_BR
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