Maximizing Boosted Influence Spread with Edge Addition in Online Social Networks

Abstract

Influence maximization with application to viral marketing is a well-studied problem of finding a small number of influential users in a social network to maximize the spread of influence under certain influence cascade models. However, almost all previous studies have focused on node-level mining, where they consider identifying nodes as the initial seeders to achieve the desired outcomes. In this article, instead of targeting nodes, we investigate a new boosted influence maximization problem from the edge-level perspective, which asks for finding an edge set that is added to the network to maximize the increased influence spread of a given seed set. We show that the problem is NP-hard and the influence spread function no longer exhibits the property of submodularity, which impose more challenging on the problem. Therefore, we devise a restricted form that is submodular and propose a greedy algorithm with approximate guarantee to solve the problem. However, because of its poor computational efficiency, we further propose an improved greedy algorithm that integrates several effective optimization strategies to significantly speed up the edge selection without sacrificing its accuracy. Extensive experiments over real-world available social networks of different sizes demonstrate the effectiveness and efficiency of the proposed methods.