000075620 001__ 75620
000075620 005__ 20191127155458.0
000075620 0247_ $$2doi$$a10.1093/comnet/cnx024
000075620 0248_ $$2sideral$$a105221
000075620 037__ $$aART-2018-105221
000075620 041__ $$aeng
000075620 100__ $$ade Arruda, G.F.
000075620 245__ $$aA general Markov chain approach for disease and rumour spreading in complex networks
000075620 260__ $$c2018
000075620 5060_ $$aAccess copy available to the general public$$fUnrestricted
000075620 5203_ $$aSpreading processes are ubiquitous in natural and artificial systems. They can be studied via a plethora of models, depending on the specific details of the phenomena under study. Disease contagion and rumour spreading are among the most important of these processes due to their practical relevance. However, despite the similarities between them, current models address both spreading dynamics separately. In this article, we propose a general spreading model that is based on discrete time Markov chains. The model includes all the transitions that are plausible for both a disease contagion process and rumour propagation. We show that our model not only covers the traditional spreading schemes but that it also contains some features relevant in social dynamics, such as apathy, forgetting, and lost/recovering of interest. The model is evaluated analytically to obtain the spreading thresholds and the early time dynamical behaviour for the contact and reactive processes in several scenarios. Comparison with Monte Carlo simulations shows that the Markov chain formalism is highly accurate while it excels in computational efficiency. We round off our work by showing how the proposed framework can be applied to the study of spreading processes occurring on social networks.
000075620 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/FIS2014-55867-P$$9info:eu-repo/grantAgreement/EC/FP7/317532/EU/Foundational Research on MULTIlevel comPLEX networks and systems/MULTIPLEX$$9info:eu-repo/grantAgreement/ES/DGA/FENOL-GROUP
000075620 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000075620 592__ $$a0.608$$b2018
000075620 593__ $$aApplied Mathematics$$c2018$$dQ1
000075620 593__ $$aComputational Mathematics$$c2018$$dQ1
000075620 593__ $$aManagement Science and Operations Research$$c2018$$dQ1
000075620 593__ $$aControl and Optimization$$c2018$$dQ1
000075620 593__ $$aComputer Networks and Communications$$c2018$$dQ1
000075620 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000075620 700__ $$aRodrigues, F.A.
000075620 700__ $$aRodriguez, P.M.
000075620 700__ $$0(orcid)0000-0002-5655-1587$$aCozzo, E.
000075620 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Y.$$uUniversidad de Zaragoza
000075620 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000075620 773__ $$g6, 2 (2018), 215-242$$pJ. complex. netw$$tJOURNAL OF COMPLEX NETWORKS$$x2051-1310
000075620 8564_ $$s1434706$$uhttps://zaguan.unizar.es/record/75620/files/texto_completo.pdf$$yVersión publicada
000075620 8564_ $$s69915$$uhttps://zaguan.unizar.es/record/75620/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000075620 909CO $$ooai:zaguan.unizar.es:75620$$particulos$$pdriver
000075620 951__ $$a2019-11-27-15:48:40
000075620 980__ $$aARTICLE