Towards Agent-Based Models for Synthetic Social Network Generation

Agent-based modeling is a powerful tool to perform simulations over heterogeneous autonomous entities and, consequently, it can be used to analyze intrinsically emergent phenomena such as social networks. In the present work we present a meta-model that takes into account features of existing network models and describe an agent-based generation system built around such a meta-model. Our system is meant to provide a framework to ease the transition between analytic network models and newer agent-based models, where the nodes are autonomous, pro-active and potentially learning agents.

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