The rapidly growing field of network analytics requires data sets for use inevaluation. Real world data often lack truth and simulated data lack narrativefidelity or statistical generality. This paper presents a novel,mixed-membership, agentbased simulation model to generate activity data withnarrative power while providing statistical diversity through random draws. Themodel generalizes to a variety of network activity types such as Internet andcellular communications, human mobility, and social network interactions. Thesimulated actions over all agents can then drive an application specificobservational model to render measurements as one would collect in real-worldexperiments. We apply this framework to human mobility and demonstrate itsutility in generating high fidelity traffic data for network analytics.
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