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

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