Controlling a Social Network of Individuals With Coevolving Actions and Opinions

We consider a population of individuals who have actions and opinions, which coevolve, mutually influencing one another on a complex social network. In particular, we formulate a control problem in which we assume that we can inject into the network a committed minority —a set of stubborn nodes— with the objective of steering the population, initially at a consensus, to a different consensus state. Our study focuses on two main objectives: i) determining the conditions under which the committed minority succeeds in its goal, and ii) identifying the optimal placement for such a committed minority. After deriving general monotone convergence result for the controlled dynamics, we leverage these results to build a computationally-efficient algorithm to solve the first problem and an effective heuristic for the second problem, which we prove to be NP-complete. For both algorithms, we establish theoretical guarantees and we demonstrate them though academic and real-world case studies.