Abstract
According to the development of the Internet, using social networks has become an efficient way to marketing these days. The problem of Influence Maximization (IM) appeared in marketing diffusion is one of hot subjects. Nevertheless, there are no researches on propagating information whereas limits unwanted users. Moreover, recent researches shows that information spreading seems to dim after some steps. Hence, how to maximize the influence while limits opposite users after a number of steps? The problem has real applications because business companies always mutually compete and extremely potential desire to broad cart their product without the leakage to opponents.
To be motivated by the phenomenon, we proposed a problem called Influence Maximization while unwanted users limited (d-IML) during known propagation hops d. The problem would be proved to be NP-Complete and could not be approximated with the rate \(1-1/e\) and its objective function was sub modular. Furthermore, we recommended an efficient algorithms to solve the problem. The experiments were handled via the real social networks datasets and the results showed that our algorithm generated better outcome than several other heuristic methods.
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Pham, C.V., Thai, M.T., Ha, D., Ngo, D.Q., Hoang, H.X. (2016). Time-Critical Viral Marketing Strategy with the Competition on Online Social Networks. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_10
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DOI: https://doi.org/10.1007/978-3-319-42345-6_10
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