Abstract
This paper deals with the statistical analysis of social networks, and it consists of two parts. First, a survey of the existing, power-law -inspired approaches to the modeling of degree distributions of social networks is conducted. It is argued, with the support of a simple experiment, that these approaches can hardly accommodate and comprehensively explain the range of phenomena observed in empirical social networks. Second, an alternative modeling framework is presented. The observed, macro-level behavior of social networks is described in terms of the individual, “hidden” dynamics, and the necessary equations are given. It is demonstrated, via experiments, that a Laplace-Stieltjes hypertransform of the distribution function of human decision-making or reaction time often provides for an adequate model in statistical analysis of social systems. The study results are briefly discussed, and conclusions are drawn.
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Kryssanov, V.V., Rinaldo, F.J., Kuleshov, E.L., Ogawa, H. (2008). A Hidden Variable Approach to Analyze “Hidden” Dynamics of Social Networks. In: Friemel, T.N. (eds) Why Context Matters. VS Verlag für Sozialwissenschaften. https://doi.org/10.1007/978-3-531-91184-7_2
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DOI: https://doi.org/10.1007/978-3-531-91184-7_2
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