Organizing an Influential Social Event Under a Budget Constraint

Recently, the proliferation of event-based social services has made it possible for organizing personalized offline events through the users' information shared online. In this paper, we study the budget-constrained influential social event organization problem, where the goal is to select a group of influential users with required features to organize a social event under a budget $B$B. We show that our problem is NP-hard and can be formulated as a submodular maximization problem with mixed packing and covering constraints. We then propose several polynomial time algorithms for our problem with provable approximation ratios, which adopt a novel "surrogate optimization" approach and the method of reverse-reachable set sampling. Moreover, we also consider the case where the influence spread function is unknown and can be arbitrarily selected from a set of candidate submodular functions, and extend our algorithms to address a "robust influential event organization" problem under this case. Finally, we conduct extensive experiments using real social networks to test the performance of our algorithms, and the experimental results demonstrate that our algorithms significantly outperform the prior studies both on the running time and on the influence spread.