Models and algorithms for election control through influence maximization

We present our ongoing work on the election control problem via social influence. We consider the problem of exploiting social influence in a network of voters to change their opinion about a target candidate with the aim of increasing his chance to win or lose the election. We introduce the Linear Threshold Ranking and the Probabilistic Linear Threshold Rankings, natural and powerful extensions of the well-established Linear Threshold Model. In both models we are able to maximize the score of a target candidate by showing submodularity. We exploit such property to provide a constant factor approximation algorithm for the constructive and destructive election control problems. We outline some further research directions which we are investigating.