Abstract
Controlling networked dynamical systems is a challenging endeavor, specifically keeping in mind the fact that in many scenarios the actors engaged in the dynamism behave selfishly, only taking into account their own individual utility. This setting has been widely studied in the field of game theory. One way we can control system dynamics is through the use of control parameters that are at our disposal, but finding optimal values for these parameters is complex and time consuming. In this paper we use the relation between network structural properties and control parameters to create a mathematical model that speeds up the calculation of the aforementioned values. For this, we use learning methods to find optimal values that can control the system dynamics based on the correlation between structurally similar networks.
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