No abstract

We present a network software suite that can model contagions or opinion manipulation in social networks, that combines features from the standard packages and extends them to allow complex, interacting, dynamic topologies, and dynamic heterogeneous agent types, with individual interaction policies. The framework allows for the easy implementation of new agent types, and provides flexible visualization tools to elucidate network behavior over time.

The study of social networks has increased rapidly in the past few decades. Of recent interest are the dynamics of changing opinions over a network. Some research has investigated how interpersonal influence can affect opinion change, how to maximize/minimize the spread of opinion change over a network, and recently, if/how agents can act strategically to effect some outcome in the network's opinion distribution. This latter problem can be modeled and addressed as a reinforcement learning problem; we introduce an approach to help network agents find strategies that outperform hand-crafted policies. Our preliminary results show that our approach is promising in networks with dynamic topologies.