Efficient Algorithm for Multiple Benefit Thresholds Problem in Online Social Networks

In the context of viral marketing in Online Social Networks (OSNs), companies often find some users (called a seed set) to initiate the spread of their product’s information so that the benefit gained exceeds a given threshold. However, in a realistic scenario, marketing strategies often change so the selection of a seed set for a particular threshold is not enough to provide an effective solution. Motivated by this phenomenon, we investigate the Multiple Benefit Thresholds (MBT), defined as follows: Given a social network under an information diffusion and a set of thresholds T = {T1, T2, … , Tk}, the problem finds seed sets S1, S2, … , Sk with the minimal cost so that their benefit gained after the influence process are at least T1, T2, … , Tk, respectively. To find the solution, we propose an efficient algorithm with theoretical guarantees, named Efficient Sampling for Selecting Multiple seed sets (ESSM) by developing an algorithmic framework and utilizing the sampling technique for estimating the objective function. We perform extensive experiments using some real networks show that the effective and performance of our algorithm, which outperforms other algorithms in term both the cost and running time.