Link Recommendation for Social Influence Maximization

Abstract

Social link recommendation systems, like “People-you-may-know” on Facebook, “Who-to-follow” on Twitter, and “Suggested-Accounts” on Instagram assist the users of a social network in establishing new connections with other users. While these systems are becoming more and more important in the growth of social media, they tend to increase the popularity of users that are already popular. Indeed, since link recommenders aim to predict user behavior, they accelerate the creation of links that are likely to be created in the future and, consequently, reinforce social bias by suggesting few (popular) users, giving few chances to most users to create new connections and increase their popularity. In this article, we measure the popularity of a user by means of her social influence, which is her capability to influence other users’ opinions, and we propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood of being created. In detail, we give a factor approximation algorithm for the problem of maximizing the social influence of a given set of target users by suggesting a fixed number of new connections considering the Linear Threshold model as model for diffusion. We experimentally show that, with few new links and small computational time, our algorithm is able to increase by far the social influence of the target users. We compare our algorithm with several baselines and show that it is the most effective one in terms of increased influence.