Abstract
Influence maximization aims to seek k nodes from a social network such that the expected number of activated nodes by these k nodes is maximized. However, influence maximization is different from profit maximization for a real marketing campaign. We observe that when promotion time increases, the number of activated nodes tends to be stable eventually. In this paper, we first use real action log to propose a novel influence power allocation model with time span called IPA-T, and then present time optimal profit maximization problem called TOPM based on IPA-T. To address this problem, we propose an effective approximation algorithm called Profit-Max. Experimental results on real datasets verify the effectiveness and efficiency of Profit-Max.
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Acknowledgment
This work was supported by the Natural Science Foundation of Heilongjiang Province (No. F201430), and the Innovation Talents Project of Science and Technology Bureau of Harbin (No. 2017RAQXJ094).
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Liu, Y., Liu, Z., Xie, S., Li, X. (2019). Time Optimal Profit Maximization in a Social Network. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_19
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DOI: https://doi.org/10.1007/978-3-030-26072-9_19
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