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Recent technological changes have increased connectivity between individuals around the world leading to higher frequency interactions between members of communities that would be otherwise distant and disconnected. This paper examines a model of opinion dynamics in interacting communities and studies how increasing interaction frequency affects the ability for communities to retain distinct identities versus falling into consensus or polarized states in which community identity is lost. We also study the effect (if any) of opinion noise related to a tendency for individuals to assert their individuality in homogenous populations. Our work builds on a model we developed previously [11] where the dynamics of opinion change is based on individual interactions that seek to minimize some energy potential based on the differences between opinions across the population.
We investigate the impact of noise and topology on opinion diversity in social networks. We do so by extending well-established models of opinion dynamics to a stochastic setting where agents are subject both to assimilative forces by their local soc
Hidden community is a new graph-theoretical concept recently proposed [4], in which the authors also propose a meta-approach called HICODE (Hidden Community Detection) for detecting hidden communities. HICODE is demonstrated through experiments that
Community detection is a significant and challenging task in network research. Nowadays, plenty of attention has been focused on local methods of community detection. Among them, community detection with a greedy algorithm typically starts from the i
Anti-social behaviors in social media can happen both at user and community levels. While a great deal of attention is on the individual as an aggressor, the banning of entire Reddit subcommunities (i.e., subreddits) demonstrates that this is a multi
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