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Generalized mean-field approximation for the Deffuant opinion dynamics model on networks

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Show simple item record Fennell, Susan C. Burke, Kevin Quayle, Michael Gleeson, James P. 2021-03-18T15:49:48Z 2021-03-18T15:49:48Z 2021
dc.description peer-reviewed en_US
dc.description.abstract When the interactions of agents on a network are assumed to follow the Deffuant opinion dynamics model, the outcomes are known to depend on the structure of the underlying network. This behavior cannot be captured by existing mean-field approximations for the Deffuant model. In this paper, a generalized mean-field approximation is derived that accounts for the effects of network topology on Deffuant dynamics through the degree distribution or community structure of the network. The accuracy of the approximation is examined by comparison with large-scale Monte Carlo simulations on both synthetic and real-world networks. en_US
dc.language.iso eng en_US
dc.publisher American Chemical Society en_US
dc.relation 802421 en_US
dc.relation.ispartofseries Physical Review E;103, 012314
dc.subject mathematics en_US
dc.title Generalized mean-field approximation for the Deffuant opinion dynamics model on networks en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1103/PhysRevE.103.012314
dc.contributor.sponsor IRC en_US
dc.contributor.sponsor SFI en_US
dc.contributor.sponsor ERC en_US
dc.contributor.sponsor European Union (EU) en_US
dc.contributor.sponsor Horizon 2020 en_US
dc.relation.projectid 16/IA/4470 en_US
dc.relation.projectid 16/RC/3918 en_US
dc.relation.projectid 18/CRT/6049 en_US
dc.relation.projectid 802421 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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