Optimizing influence diffusion in a social network with fuzzy costs for targeting nodes

Over the last decade, the problem of optimizing influence diffusion in a social network has drawn much attention. In this paper, we study the problem of minimizing the complete influence time in a social network where the cost for targeting each individual is with fuzzy uncertainty. By adopting three different decision criteria in the area of uncertain programming, we propose three decision models to characterize the problem we study. In view of the complexity of the problem, we design a hybrid intelligence algorithm to solve models, where fuzzy simulation technologies are integrated with a modified greedy algorithm. Finally, numerical experiments are preformed to show the effectiveness of the models and algorithm we propose.