Stochastic agent-based simulations of social networks

mobility The rapidly growing field of network analytics requires data sets for use in evaluation. Real world data often lack truth and simulated data lack narrative fidelity or statistical gener-ality. This paper presents a novel, mixed-membership, agent-based simulation model to generate activity data with narra-tive power while providing statistical diversity through ran-dom draws. The model generalizes to a variety of network ac-tivity types such as Internet and cellular communications, hu-man mobility, and social network interactions. The simulated actions over all agents can then drive an application specific observational model to render measurements as one would collect in real-world experiments. We apply this framework to human mobility and demonstrate its utility in generating high fidelity traffic data for network analytics. 1 1