Activation Probability Maximization for Target Users Under Influence Decay Model

In this paper, we study how to activate a specific set of targeting users \(\mathcal {T}\), e.g., selling a product to a specific target group, is a practical problem for using the limited budget efficiently. To address this problem, we first propose the Activation Probability Maximization (APM) problem, i.e., to select a seed set S such that the activation probability of the target users in \(\mathcal {T}\) is maximized. Considering that the influence will decay during information propagation, we propose a novel and practical Influence Decay Model (IDM) as the information diffusion model in the APM problem. Based on the IDM, we show that the APM problem is NP-hard and the objective function is monotone non-decreasing and submodular. We provide a (\(1-1/e\))-approximation Basic Greedy Algorithm (BGA). Furthermore, a speed-up Scalable Algorithm (SA) is proposed for online large social networks. Finally, we run our algorithms by simulations on synthetic and real-life social networks to evaluate the effectiveness and efficiency of the proposed algorithms. Experimental results validate our algorithms are superior to the comparison algorithms.