Efficient influence maximization under TSCM: a suitable diffusion model in online social networks

Abstract

The study on influence modeling is to understand the information diffusion and word-of-mouth marketing. Independent Cascade Model (ICM) is the most widely studied theoretical diffusion model. However, until now it is unknown whether ICM matches the real information diffusion in online social networks. In this paper, we demonstrate that ICM cannot model accurately for the structure of information diffusion over real networks through our experiments. Meanwhile, we propose a more suitable diffusion model named Three Steps Cascade Model (TSCM) to simulate information diffusion process in online social networks. We focus on the influence maximization problem under TSCM. First, we show that this optimization problem is NP hard. Then we prove that the greedy algorithm can guarantee an influence spread within 63 % of the optimal value. Finally we devise an efficient algorithm which is scalable for large social networks. The experiment results on large-scale real networks show the robustness and utility of our approach.