Individual influence maximization via link recommendation

Abstract

Recent years have witnessed the increasing interest in exploiting social influence in social networks for many applications, such as viral marketing. Most of the existing research focused on identifying a subset of influential individuals with the maximum influence spread. However, in the real-world scenarios, many individuals also care about the influence of herself and want to improve it. In this paper, we consider such a problem that maximizing a target individual’s influence by recommending new links. Specifically, if a given individual/node makes new links with our recommended nodes then she will get the maximum influence gain. Along this line, we formulate this link recommendation problem as an optimization problem and propose the corresponding objective function. As it is intractable to obtain the optimal solution, we propose greedy algorithms with a performance guarantee by exploiting the submodular property. Furthermore, we study the optimization problem under a specific influence propagation model (i.e., Linear model) and propose a much faster algorithm (uBound), which can handle large scale networks without sacrificing accuracy. Finally, the experimental results validate the effectiveness and efficiency of our proposed algorithms.