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
Descripción
Sumario: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.