  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 generality. This paper presents a novel,
mixed-membership, agentbased simulation model to generate activity data with
narrative power while providing statistical diversity through random draws. The
model generalizes to a variety of network activity types such as Internet and
cellular communications, human 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.
