Abstract
Control of collective behavior is an active topic in biology, social, and computer science. In this work we investigate how a minority of informed agents can influence and control the whole society through local interactions. The problem we specifically target is that a minority of people with a bounded budget for initiating new social relations attempt to control the collective behavior of a society and move the crowd toward a specific goal. Assuming that local interactions can only take place between friends, the minority has to initiate some new relations with the majority. The total cost of new relations is limited to a budget. The problem is then finding the optimal links in order to gain maximum impact on the society. We will model the problem as a diffusion process in a social network. The proof of NP-hardness of the problem for Local Interaction Game model of diffusion is presented. Simulations show that the proposed method surpasses the popular strategies based on degree and distance centrality in performance.
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Asiaee Taheri, A., Afshar, M., Asadpour, M. (2013). Influence Maximization for Informed Agents in Collective Behavior. In: Martinoli, A., et al. Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32723-0_28
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DOI: https://doi.org/10.1007/978-3-642-32723-0_28
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