Earned Benefit Maximization in Social Networks Under Budget Constraint
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Given a social network with nonuniform selection cost of the users, the problem of Budgeted Influence Maximization (BIM in short) asks for selecting a subset of the nodes within an allocated budget for initial activation, such that due to the cascading effect, influence in the network is maximized. In this paper, we study this problem with a variation, where a set of nodes are designated as target nodes, each of them is assigned with a benefit value, that can be earned by influencing them, and our goal is to maximize the earned benefit by initially activating a set of nodes within the budget. We call this problem as the Earned Benefit Maximization Problem. First, we show that this problem is NPHard and the benefit function is monotone, submodular under the Independent Cascade Model of diffusion. We propose an incremental greedy strategy for this problem and show, with minor modification it gives (1-1/√(e))factor approximation guarantee on the earned benefit. Next, by exploiting the submodularity property of the benefit function, we improve the efficiency of the proposed greedy algorithm. Then, we propose a hopbased heuristic method, which works based on the computation of the `expected earned benefit' of the effective neighbors corresponding to the target nodes. Finally, we perform a series of extensive experiments with four reallife, publicly available social network datasets. From the experiments, we observe that the seed sets selected by the proposed algorithms can achieve more benefit compared to many existing methods. Particularly, the hopbased approach is found to be more efficient than the other ones for solving this problem.
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