Improving the influence under IC-N model in social networks

摘要

The influence maximization problem in social networks is to find a set of seed nodes such that the total influence effect is maximized under certain cascade models. In this paper, we propose a novel task of improving influence, which is to find strategies to allocate the investment budget under IC-N model. We prove that our influence improving problem is NP-hard, and propose new algorithms under IC-N model. To the best of our knowledge, our work is the first one that studies influence improving problem under bounded budget when negative opinions emerge. Finally, we implement extensive experiments over a large data collection obtained from real-world social networks, and evaluate the performance of our approach.