Open-Loop Equilibrium Strategies for Dynamic Influence Maximization Game over Social Networks

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We consider the problem of budget allocation for competitive influence maximization over social networks. In this problem, multiple competing parties (players) want to distribute their limited advertising resources over a set of social individuals to maximize their long-run cumulative payoffs. It is assumed that the individuals are connected via a social network and update their opinions based on the classical DeGroot model. The players must decide on the budget distribution among the individuals at a finite number of campaign times to maximize their overall payoff as a function of individuals' opinions. Under some assumptions, we show that i) the optimal investment strategy for a single player can be found in polynomial time by solving a concave program, and ii) the open-loop equilibrium strategies for the multiplayer dynamic game can be computed efficiently by following natural regret-minimization dynamics. Our results extend earlier work on the static version of the problem to a dynamic multistage game.

Original languageEnglish (US)
Pages (from-to)1496-1500
Number of pages5
JournalIEEE Control Systems Letters
StatePublished - 2022


  • Opinion dynamics
  • convex optimization
  • dynamic games
  • network resource allocation
  • open-loop Nash equilibrium
  • social networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization


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