Social reinforcement with weighted interactions

The speed and extent of diffusion of behaviors in social networks depends on network structure and individual preferences. The contribution of the present study is twofold. First, we introduce weighted interactions between potential adopters that depend on the similarity in their preferences and mod...

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Detalles Bibliográficos
Autores: Konc, Théo|||0000-0002-0203-9473, Savin, Ivan|||0000-0002-9469-0510
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:213304
Acceso en línea:https://ddd.uab.cat/record/213304
https://dx.doi.org/urn:doi:10.1103/PhysRevE.100.022305
Access Level:acceso abierto
Palabra clave:Clustering
Degree distributions
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spelling Social reinforcement with weighted interactionsKonc, Théo|||0000-0002-0203-9473Savin, Ivan|||0000-0002-9469-0510ClusteringDegree distributionsThe speed and extent of diffusion of behaviors in social networks depends on network structure and individual preferences. The contribution of the present study is twofold. First, we introduce weighted interactions between potential adopters that depend on the similarity in their preferences and moderate the strength of social reinforcement. The reason for the extension is the existence of a confirmation bias in the way agents treat information by prioritizing evidence conforming to their opinion. As a result, individuals become less likely to be influenced by peers with relatively different preferences, reducing the overall diffusion rate under clustered networks. Second, we enrich our analysis by also considering a scale free network topology with a high degree asymmetry, motivated by its pervasiveness in online social networks. This network performs consistently well in terms of diffusion for different parameter combinations and clearly outperforms clustered networks under weighted interactions. Our results show that more realistic assumptions regarding agents' interactions shift the focus from clustering to degree distribution in the study of network structures allowing for fast and widespread behavior adoption. 22019-01-0120192019-01-01Articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/213304https://dx.doi.org/urn:doi:10.1103/PhysRevE.100.022305reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengEuropean Commission https://doi.org/10.13039/501100000780 741087open accesshttp://purl.org/coar/access_right/c_abf2Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.https://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2133042026-06-06T12:50:31Z
dc.title.none.fl_str_mv Social reinforcement with weighted interactions
title Social reinforcement with weighted interactions
spellingShingle Social reinforcement with weighted interactions
Konc, Théo|||0000-0002-0203-9473
Clustering
Degree distributions
title_short Social reinforcement with weighted interactions
title_full Social reinforcement with weighted interactions
title_fullStr Social reinforcement with weighted interactions
title_full_unstemmed Social reinforcement with weighted interactions
title_sort Social reinforcement with weighted interactions
dc.creator.none.fl_str_mv Konc, Théo|||0000-0002-0203-9473
Savin, Ivan|||0000-0002-9469-0510
author Konc, Théo|||0000-0002-0203-9473
author_facet Konc, Théo|||0000-0002-0203-9473
Savin, Ivan|||0000-0002-9469-0510
author_role author
author2 Savin, Ivan|||0000-0002-9469-0510
author2_role author
dc.subject.none.fl_str_mv Clustering
Degree distributions
topic Clustering
Degree distributions
description The speed and extent of diffusion of behaviors in social networks depends on network structure and individual preferences. The contribution of the present study is twofold. First, we introduce weighted interactions between potential adopters that depend on the similarity in their preferences and moderate the strength of social reinforcement. The reason for the extension is the existence of a confirmation bias in the way agents treat information by prioritizing evidence conforming to their opinion. As a result, individuals become less likely to be influenced by peers with relatively different preferences, reducing the overall diffusion rate under clustered networks. Second, we enrich our analysis by also considering a scale free network topology with a high degree asymmetry, motivated by its pervasiveness in online social networks. This network performs consistently well in terms of diffusion for different parameter combinations and clearly outperforms clustered networks under weighted interactions. Our results show that more realistic assumptions regarding agents' interactions shift the focus from clustering to degree distribution in the study of network structures allowing for fast and widespread behavior adoption.
publishDate 2019
dc.date.none.fl_str_mv 2
2019-01-01
2019
2019-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
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dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/213304
https://dx.doi.org/urn:doi:10.1103/PhysRevE.100.022305
url https://ddd.uab.cat/record/213304
https://dx.doi.org/urn:doi:10.1103/PhysRevE.100.022305
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 741087
dc.rights.none.fl_str_mv open access
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eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
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