  Online social networks (OSNs) such as YoutTube, Instagram, Twitter, Facebook,
etc., serve as important platforms for users to share their information or
content to friends or followers. Oftentimes, users want to enhance their social
visibility, as it can make their contents, i.e., opinions, videos, pictures,
etc., attract attention from more users, which in turn may bring higher
commercial benefit to them. Motivated by this, we propose a mechanism, where
the OSN operator provides a "social visibility boosting service" to incentivize
"transactions" between requesters (users who seek to enhance their social
visibility via adding new "neighbors") and suppliers (users who are willing to
be added as a new "neighbor" of any requester when certain "rewards" is
provided). We design a posted pricing scheme for the OSN provider to charge the
requesters who use such boosting service, and reward the suppliers who
contribute to such boosting service. The OSN operator keeps a fraction of the
payment from requesters and distributes the remaining part to participating
suppliers "fairly" via the Shapley value. The objective of the OSN provider is
to select the price and supplier set to maximize the total amount of revenue
under the budget constraint of requesters. We first show that the revenue
maximization problem is not simpler than an NP-hard problem. We then decomposed
it into two sub-routines, where one focuses on selecting the optimal set of
suppliers, and the other one focuses on selecting the optimal price. We prove
the hardness of each sub-routine, and eventually design a computationally
efficient approximation algorithm to solve the revenue maximization problem
with provable theoretical guarantee on the revenue gap. We conduct extensive
experiments on four public datasets to validate the superior performance of our
proposed algorithms.
