Abstract
Today, online social networks (OSNs) play an important role in diffusing information among people. Extensive research on social network analysis shows that online users trust to sharing information of acquaintances and they also may share that information. The repetitive process of information diffusion by a limited number of users can be very influential. In fact, with the rapid development and increasing popularity of online social networks, an enormous amount of information has been generated and diffused by human interactions through these networks. One of the fundamental problems in information diffusion in online social networks is the influence maximization problem. The problem of optimizing influence maximization in a social network focuses on how to select a small sub-set of individuals as the initial influence adopters to trigger a cascade such that the influence diffusion in the social network is maximized. In this chapter, two learning automata based approaches for influence maximization are introduced. The first one using discretized generalized confidence pursuit algorithm (DGCPA) tries to select proper seed set nodes and the second algorithm chooses heuristically seed set nodes using the results of minimum positive influence dominating set (MPIDS) with the aid of learning automata. Also the algorithm is investigated through both the mathematical analysis and simulation as well.
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Rezvanian, A., Moradabadi, B., Ghavipour, M., Daliri Khomami, M.M., Meybodi, M.R. (2019). Social Influence Maximization. In: Learning Automata Approach for Social Networks. Studies in Computational Intelligence, vol 820. Springer, Cham. https://doi.org/10.1007/978-3-030-10767-3_9
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