A Hidden Variable Approach to Analyze “Hidden” Dynamics of Social Networks

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.KeywordsSocial NetworkDegree DistributionSocial Network AnalysisGamma MixtureHuman Reaction TimeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.