Budgeted influence and earned benefit maximization with tags in social networks

Influence Maximization Problem aims at identifying a limited number of highly influential users who will be working for diffusion agents to maximize the influence. In case Budgeted (BIM), network have cost and user selection needs done within given budget. Earned Benefit (EBM) Problem, set target along with their benefit value is aim choose an allocated budget earned benefit. this paper, we study BIM EBM under tag-specific edge probability setting, which means instead single values (each one specific context e.g., ‘games,’ ‘academics,’ etc.) per given. The identify tags maximizing influence Considering realistic fact that different impact on communities social network, propose two solution methodologies pruning technique. A detailed analysis all approaches has been done. An extensive experiments carried out three benchmark datasets. From experiments, observe proposed outperform baseline methods (e.g., random node-random tag, high-degree node–high-frequency tag community). For tag-based improvement upto $$8\%$$ in terms influenced nodes $$15\%$$