Identifying influential users in unknown social networks for adaptive incentive allocation under budget restriction

Abstract

In recent years, recommenze the social influence among users to enhance the effect of incentivization. Through incentivizing influential users directly, their followers in the social network are possibly incentivized indirectly. However, in many real-world applications, identifying influential users can be challenging because of the unknown network topology. In this paper, we propose a novel algorithm for exploring influential users in unknown networks, estimating the influential relationships among users based on their historical behaviors without knowing the network topology. In addition, we design an adaptive incentive allocation approach that determines incentive values based on each user’s preferences and influence ability. We evaluate the performance of the proposed approaches by conducting experiments on synthetic and real-world datasets. The experimental results demonstrate the effectiveness of the proposed approaches.