Diversified social influence maximization

Abstract

For better viral marketing, there has been a lot of research on social influence maximization. However, the problem that who is influenced and how diverse the influenced population is, which is important in real-world marketing, has largely been neglected. To that end, in this paper, we propose to consider the magnitude of influence and the diversity of the influenced crowd simultaneously. Specifically, we formulate it as an optimization problem, i.e., diversified social influence maximization. First, we present a general framework for this problem, under which we construct a class of diversity measures to quantify the diversity of the influenced crowd. Meanwhile, we prove that a simple greedy algorithm guarantees to provide a near-optimal solution to the optimization problem. Furthermore, we relax the problem by focusing on the diversity of the nodes targeted for initial activation, and show how this relaxed form could be used to diversify the results of many heuristics, e.g., PageRank. Finally, we run extensive experiments on two real-world datasets, showing that our formulation is effective in generating diverse results.