Information Communities in Social Networks. Part I: From Concept to Mathematical Models
Gubanov, D.A. and Petrov, I.V. Information Communities in Social Networks. Part I: From Concept to Mathematical Models
Abstract. This survey covers the literature related to information communities in mutually complementary areas: the formation of information communities in social networks and some applied aspects of identifying and analyzing information communities in social networks. First, mathematical models describing the formation of information communities under uncertainty are considered. Among these models, the most relevant ones are the mathematical models of opinion/belief dynamics reflecting any changes in the beliefs of nodes under the influence of other network nodes and significant effects (in particular, the preservation of differences in beliefs and the divergence of beliefs) that lead to the formation of information communities. In part I of the survey, the concept of an information community is first presented. Then information processing and decision-making by an agent in a social network under external uncertainty are outlined. The factors influencing the formation of information communities in the network are highlighted, and the basic models of Bayesian agents and their extensions are investigated.
Keywords: social networks, information community, formation of information communities, analysis of information communities, belief formation.
Funding. This work was supported by the Russian Foundation for Basic Research, project no. 19-17-50225.
Cite this article
Gubanov, D.A., Petrov, I.V. Information Communities in Social Networks. Part I: From Concept to Mathematical Models. Control Sciences 1, 13–20 (2021). http://doi.org/10.25728/cs.2021.1.2