Efficient Revenue Maximization for Viral Marketing in Social Networks

In social networks, the problem of revenue maximization aims at maximizing the overall revenue from the purchasing behaviors of users under the influence propagations. Previous studies use a number of simulations on influence cascades to obtain the maximum revenue. However, these simulation-based methods are time-consuming and can't be applied to large-scale networks. Instead, we propose calculation-based algorithms for revenue maximization, which gains the maximum revenue through fast approximate calculations within local acyclic graphs instead of the slow simulations across the global network. Furthermore, a max-Heap updating scheme is proposed to prune unnecessary calculations. These algorithms are designed for both the scenarios of unlimited and constrained commodity supply. Experiments on both the synthetic and real-world datasets demonstrate the efficiency and effectiveness of our proposals, that is, our algorithms run in orders of magnitude faster than the state-of-art baselines, and meanwhile, the maximum revenue achieved is nearly not affected.