Earned benefit maximization in social networks under budget constraint

Given a social network where the users are associated with non-uniform selection cost, problem of Budgeted Influence Maximization (BIM in short) asks for selecting subset nodes within an allocated budget initial activation, such that due to cascading effect, influence is maximized. In this paper, we study variation, marked as target users, each them assigned benefit and can be earned by influencing them. The goal here maximize initially activating set budget. This referred Earned Benefit Problem. First, show NP-Hard function follows monotonicity, sub-modularity property under Independent Cascade Model diffusion. We propose incremental greedy strategy show, minor modification it gives (1−1e)-factor approximation guarantee on benefit. Next, exploiting function, improve efficiency proposed algorithm. Then, hop-based heuristic method, which works based computation ‘expected benefit’. Finally, perform series extensive experiments four publicly available, real-life datasets. From experiments, observe seed sets selected algorithms achieve more compared many existing methods. Particularly, approach found efficient than other ones solving problem.