Statistical modeling of social networks activities

This paper introduces a new paradigm to characterize and understand the dynamics of a complex social network where we set up a mathematical platform that captures the network dynamics. We propose a novel generic non-parametric model to characterize a general system of social communicators. We divide the network into low-level entities, each of which has some independent features. The different entities are then combined using Bayesian nonparametric statistics, namely Dirichlet processes mixture models (DPMM). This set up was tested using a simulated case study where we show examples of its utility for behavior characterization and predictions.