Influence Maximization by Link Activation in Social Networks

The propagation of innovations in social networks has been widely studied recently. Previous research mostly focuses on either maximizing the influence by identifying a set of initial adopters, or minimizing the influence by link blocking under a certain diffusion model. In our case, we address an influence maximization problem considering the link activation under the Independent Cascade model. For this problem, we propose an approximate solution based on the computation of a cost-degree coefficient for selecting links to be activated. Simulations performed on a real network show that our algorithm performs well.