Zealots’ effect on opinion dynamics in complex networks

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dc.citation.epage214
dc.citation.issue2
dc.citation.spage203
dc.contributor.affiliationУніверситет Анкари
dc.contributor.affiliationAnkara University
dc.contributor.authorМоейніфар, В.
dc.contributor.authorГюндюк, С.
dc.contributor.authorMoeinifar, V.
dc.contributor.authorGündüç, S.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2023-10-24T07:21:54Z
dc.date.available2023-10-24T07:21:54Z
dc.date.created2021-03-01
dc.date.issued2021-03-01
dc.description.abstractУ цій статті вивчається вплив фанатиків на соціальні мережі. Розглядувана соціальна мережа заснована на безмасштабних мережах із використанням методу Барабаші– Альберта та випадкових мереж із використанням методу Ердос–Рені. Використовувалася попередньо вивчена модифікована модель виборця, яка включає фанатиків —людей, які ніколи не змінюють своєї думки. Вибрано видатних особистостей (тобто хабів) як фанатиків. Таким чином, спочатку вибрано за фанатиків важливих людей із високим ступенем (хабів); пізніше — людей із високим ступенем близькості. А потім проаналізовано час досягнення консенсусу у випадку, коли фанатики вибираються як неважливі особистості та порівняно результати усіх випадків. Виявлено, що час для досягнення консенсусу в соціальних мережах однаковий для різної кількості фанатиків, але з однаковим ступенем зараженості фанатизмом. Наприклад, ефект одного фанатика зі ступенем 64 збігається з ефектом 8 фанатиків зі ступенем 8.
dc.description.abstractIn this paper, we study zealots’ effects on social networks. Our social network is based on scale-free networks using Barabasi–Albert method and random networks using Erd'os–R'enyi method. We used a pre-studied modified Voter model that includes zealots, individuals who never change their opinions. We chose prominent individuals (i.e. hubs) as zealots. In this way we first chose important individuals with high degree (hubs); second, individuals with high closeness. And then examined the consensus time compared with that zealots are chosen as non-important individuals. We found that the time to get to the consensus state in social networks is the same for different numbers of zealots but with the same degrees of contamination with zealotry. For example, one zealot’s effect with a degree of 64 is same to 8 zealots’ effects with a degree of 8.
dc.format.extent203-214
dc.format.pages12
dc.identifier.citationMoeinifar V. Zealots’ effect on opinion dynamics in complex networks / V. Moeinifar, S. Gündüç // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 8. — No 2. — P. 203–214.
dc.identifier.citationenMoeinifar V. Zealots’ effect on opinion dynamics in complex networks / V. Moeinifar, S. Gündüç // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 8. — No 2. — P. 203–214.
dc.identifier.doidoi.org/10.23939/mmc2021.02.203
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/60394
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofMathematical Modeling and Computing, 2 (8), 2021
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dc.relation.referencesen[2] Liggett T. M. Interacting Particle Systems. Springer (1985).
dc.relation.referencesen[3] Clifford P., Sudbury A. A model for spatial conflict. Biometrika. 60 (3), 581–588 (1973).
dc.relation.referencesen[4] Mobilia M. Commitment Versus Persuasion in the Three-Party Constrained Voter Model. Journal of Statistical Physics. 151, 69–91 (2013).
dc.relation.referencesen[5] Gunton J. D., San Miguel M., Sahni P. Phase Transitions and Critical Phenomena. Vol. 8. London, Academic Press (1983).
dc.relation.referencesen[6] Suchecki K., Egu´ıluz V. M., San Miguel M. Conservation laws for the voter model in complex networks. Europhysics Letters. 69 (2), 228–234 (2005).
dc.relation.referencesen[7] Mobilia M., Petersen A., Redner S. On the role of zealotry in the voter model. Journal of Statistical Mechanics: Theory and Experiment. 8, P08029–P08029 (2007).
dc.relation.referencesen[8] Mobilia M. Does a Single Zealot Affect an Infinite Group of Voters? Physical Review Letters. 91 (2), 028701 (2003).
dc.relation.referencesen[9] Masuda N. Opinion control in complex networks. New Journal of Physics. 17 (3), 033031 (2015).
dc.relation.referencesen[10] Khalil N., San Miguel M., Toral R. Zealots in the mean-field noisy voter model. Phys. Rev. E. 97 (1), 012310 (2018).
dc.relation.referencesen[11] Barab´asi A.-L., Albert R. Emergence of Scaling in Random Networks. Science. 286 (5439), 509–512 (1999).
dc.relation.referencesen[12] Barab´asi A.-L. Network Science. Vol. 1. Cambridge University Press (2016).
dc.relation.referencesen[13] Albert R., Jeong H., Barab´asi A.-L. Error and attack tolerance of complex networks. Nature. 406, 378–382 (2000).
dc.relation.referencesen[14] Albert R., Barab´asi A.-L. Statistical mechanics of complex networks. Reviews of Modern Physics. 74 (1), 47–97 (2002).
dc.relation.referencesen[15] Bavelas A. Communication Patterns in Task-Oriented Groups. The Journal of the Acoustical Society of America. 22, 725–730 (1950).
dc.relation.referencesen[16] Freeman L. C. Centrality in social networks conceptual clarification. Social Networks. 1 (3), 215–239 (1978).
dc.relation.referencesen[17] G¨und¨u¸c S., Eryi˘git R. The role of persuasion power on the consensus formation. Physica A: Statistical Mechanics and its Applications. 426, 16–24 (2015).
dc.relation.referencesen[18] G¨und¨u¸c S. The role of fanatics in consensus formation. International Journal of Modern Physics P. 26 (3), 1–18 (2014).
dc.relation.referencesen[19] Suchecki K., Egu´ıluz V. M., Miguel M. S. Voter model dynamics in complex networks: Role of dimensionality, disorder, and degree distribution. Physical Review E. 72 (3), 036132 (2005).
dc.relation.referencesen[20] Sabidussi G. The centrality index of a graph. Psychometrika. 31, 581–603 (1966).
dc.relation.referencesen[21] Gilbert E. N. Random Graphs. Ann. Math. Statist. 30 (4), 1141–1144 (1959).
dc.relation.referencesen[22] Blagus B. M. The network of collaboration: Informatica and Uporabna Informatika. Uporabna Informatika (2005).
dc.relation.referencesen[23] Porter M., Gleeson J. Dynamical Systems on Networks. Springer (2016).
dc.relation.referencesen[24] Czepiel J. A. Word-of-Mouth Processes in the Diffusion of a Major Technological Innovation. Journal Of Marketing Research. 11 (2), 172–180 (1974).
dc.relation.referencesen[25] Beauchamp M. A. An improved index of centrality. Behavioral Science. 10, 161–165 (1965).
dc.relation.referencesen[26] Mobilia M., Georgiev T. Voting and catalytic processes with inhomogeneities. Physical Review E. 71 (4), 046102 (2005).
dc.relation.referencesen[27] Cohn B., Marriott M. Networks and Centers in the Integration of Indian Civilization. Journal of Social Research (Ranchi). 1, 1–9 (1958).
dc.rights.holder© Національний університет “Львівська політехніка”, 2021
dc.subjectмодель виборця
dc.subjectфанатик
dc.subjectвипадковий
dc.subjectбез масштабу
dc.subjectмережі
dc.subjectЕрдос–Рені
dc.subjectБарабаші–Альберт
dc.subjectцентр
dc.subjectблизькість
dc.subjectцентральність
dc.subjectvoter model
dc.subjectzealot
dc.subjectrandom
dc.subjectscale-free
dc.subjectnetworks
dc.subjectErd'os–R'enyi
dc.subjectBarabasi–Albert
dc.subjecthub
dc.subjectcloseness
dc.subjectcentrality
dc.titleZealots’ effect on opinion dynamics in complex networks
dc.title.alternativeВплив фанатиків на динаміку думок у складних мережах
dc.typeArticle

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Mathematical Modeling And Computing. – 2021. – Vol. 8, No. 2