Abstract
Social influence problems, such as Influence Maximization (IM), have been widely studied. But a key challenge remains: How does a company select a small size seed set such that the acceptance probability of target users is maximized? In this paper, we first propose the Acceptance Probability Maximization (APM) problem, i.e., selecting a small size seed set S such that the acceptance probability of target users T is maximized. Then we use classical Independent Cascade (IC) model as basic information diffusion model. Based on this model, we prove that APM is NP-hard and the objective function is monotone non-decreasing as well as submodular. Considering community structure of social networks, we transform APM to Maximum Weight Hitting Set (MWHS) problem. Next, we develop a pipage rounding algorithm whose approximation ratio is (\(1-1/e\)). Finally, we evaluate our algorithms by simulations on real-life social networks. Experimental results validate the performance of the proposed algorithm.
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