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In this paper, we investigate the problem of how beliefs diffuse among members of social networks. We propose an information flow model (IFM) of belief that captures how interactions among members affect the diffusion and eventual convergence of a belief. The IFM model includes a generalized Markov Graph (GMG) model as a social network model, which reveals that the diffusion of beliefs depends heavily...
In this paper we propose a belief flow model for social networks and evaluate its application on estimation of public converged beliefs. The model reveals that the control of beliefs in a social network heavily depends on its degree centralities and clustering coefficients. The application of this model to social network belief flow simulation leads to a capacity to control and predict the converged...
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