dc.creatorCota, Wesley
dc.creatorFerreira, Silvio C.
dc.creatorÓdor, Géza
dc.date2018-04-17T13:31:44Z
dc.date2018-04-17T13:31:44Z
dc.date2016-03-28
dc.date.accessioned2023-09-27T21:50:58Z
dc.date.available2023-09-27T21:50:58Z
dc.identifier24700053
dc.identifierhttps://doi.org/10.1103/PhysRevE.93.032322
dc.identifierhttp://www.locus.ufv.br/handle/123456789/18750
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8967045
dc.descriptionWe provide numerical evidence for slow dynamics of the susceptible-infected-susceptible model evolving on finite-size random networks with power-law degree distributions. Extensive simulations were done by averaging the activity density over many realizations of networks. We investigated the effects of outliers in both highly fluctuating (natural cutoff) and nonfluctuating (hard cutoff) most connected vertices. Logarithmic and power-law decays in time were found for natural and hard cutoffs, respectively. This happens in extended regions of the control parameter space λ 1 < λ < λ 2 , suggesting Griffiths effects, induced by the topological inhomogeneities. Optimal fluctuation theory considering sample-to-sample fluctuations of the pseudothresholds is presented to explain the observed slow dynamics. A quasistationary analysis shows that response functions remain bounded at λ 2 . We argue these to be signals of a smeared transition. However, in the thermodynamic limit the Griffiths effects loose their relevancy and have a conventional critical point at λ c = 0. Since many real networks are composed by heterogeneous and weakly connected modules, the slow dynamics found in our analysis of independent and finite networks can play an important role for the deeper understanding of such systems.
dc.formatpdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherPhysical Review E.
dc.relationv. 93, n. 3, p. 032322(9), March 2016
dc.rightsAmerican Physical Society
dc.subjectSusceptible-infected-susceptible
dc.subjectGriffiths effects
dc.titleGriffiths effects of the susceptible-infected-susceptible epidemic model on random power-law networks
dc.typeArtigo


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