Social Interactions Model and Adaptability of Human Behavior - Zhao, Kun; Bianconi, Ginestra (Frontiers in Physiology, 2011)
Human social networks evolve on the fast timescale of face-to-face interactions and of interactions mediated by technology such as a telephone calls or video conferences. The resulting networks have a strong dynamical component that changes significantly the properties of dynamical processes. In this paper we study a general model of pairwise human social interaction intended to model both face-to-face interactions and mobile-phone communication. We study the distribution of durations of social interactions in within the model. This distribution in one limit is a power-law, for other values of the parameters of the model this distribution is given by a Weibull function. Therefore the model can be used to model both face-to-face interactions data, where the distribution of duration has been shown to be fat-tailed, and mobile-phone communication data where the distribution of duration is given by a Weibull distribution. The highly adaptable social interaction model propose in this paper has a very simple algorithmic implementation and can be used to simulate dynamical processes occurring in dynamical social interaction networks.