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Identifying Minimum Set of Persons that Influenced by a Promotion Campaign

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Future Data and Security Engineering (FDSE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11814))

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Abstract

As it has been proved by different researches that, they just focus on finding a different sets of the k-key players that had shown the greatest influence such as on social media network. This is equivalent finding a set of promotion people to make the maximum influenced in promotion campaigns. However, it can’t predict the outcome of the campaign in advance. In this research, we propose a new approach for a promotion campaign, that is: identifying a minimum set of players to achieve the promotion goals with “at least M people are affected by the set of players with an influence that is greater than or equal to a threshold θ”. Through this approach it can be, quantify the result of the campaign first, and then find the minimum set of persons that can influenced by promotion campaign.

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References

  1. Altshuler, Y., Pan, W., Pentland, A.S.: Trends prediction using social diffusion models. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds.) SBP 2012. LNCS, vol. 7227, pp. 97–104. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29047-3_12

    Chapter  Google Scholar 

  2. Bonacich, E.: A theory of ethnic antagonism: the split labor market. Am. Sociol. Rev. 37, 547–559 (1972)

    Article  Google Scholar 

  3. Borgatti, S.P.: Identifying sets of key players in a social network. Comput. Math. Organ. Theory 12(1), 21–34 (2006)

    Article  Google Scholar 

  4. Borgatti, S.P., Everett, M.G.: Models of core/periphery structures. Soc. Netw. 21(4), 375–395 (2000)

    Article  Google Scholar 

  5. D’Agostino, G., D’Antonio, F., De Nicola, A., Tucci, S.: Interests diffusion in social networks. Physica A: Stat. Mech. Appl. 436, 443–461 (2015)

    Article  MathSciNet  Google Scholar 

  6. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM, August 2001

    Google Scholar 

  7. Everett, M.G., Borgatti, S.P.: The centrality of groups and classes. J. Math. Sociol. 23(3), 181–201 (1999)

    Article  Google Scholar 

  8. Fang, X., Hu, P.J.H., Li, Z., Tsai, W.: Predicting adoption probabilities in social networks. Inf. Syst. Res. 24(1), 128–145 (2013)

    Article  Google Scholar 

  9. Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)

    Article  Google Scholar 

  10. Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark. Lett. 12(3), 211–223 (2001)

    Article  Google Scholar 

  11. Goldenberg, J., Libai, B., Muller, E.: Using complex systems analysis to advance marketing theory development: modeling heterogeneity effects on new product growth through stochastic cellular automata. Acad. Mark. Sci. Rev. 9(3), 1–18 (2001)

    Google Scholar 

  12. Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 241–250. ACM, February 2010

    Google Scholar 

  13. Hung, N.T., Viet, H.T.: Identifying key player using sum of influence probabilities in a social network. In: Dang, T.K., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E.J. (eds.) FDSE 2017. LNCS, vol. 10646, pp. 444–452. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70004-5_32

    Chapter  Google Scholar 

  14. Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. Theory Comput. 11(4), 105–147 (2015)

    Article  MathSciNet  Google Scholar 

  15. Latora, V., Marchiori, M.: How the science of complex networks can help developing strategies against terrorism. Chaos Solitons Fractals 20(1), 69–75 (2004)

    Article  Google Scholar 

  16. Lei, S., Maniu, S., Mo, L., Cheng, R., Senellart, P.: Online influence maximization. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 645–654. ACM, August 2015

    Google Scholar 

  17. Ortiz-Arroyo, D., Hussain, D.M.A.: An information theory approach to identify sets of key players. In: Ortiz-Arroyo, D., Larsen, H.L., Zeng, D.D., Hicks, D., Wagner, G. (eds.) EuroIsI 2008. LNCS, vol. 5376, pp. 15–26. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89900-6_5

    Chapter  Google Scholar 

  18. Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)

    Article  MathSciNet  Google Scholar 

  19. Tutzauer, F.: Entropy as a measure of centrality in networks characterized by path-transfer flow. Soc. Netw. 29(2), 249–265 (2007)

    Article  Google Scholar 

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Correspondence to Huynh Thanh Viet .

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Hung, N.T., Viet, H.T., Truong, L.N., Zghoul, M.B.Y. (2019). Identifying Minimum Set of Persons that Influenced by a Promotion Campaign. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_41

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  • DOI: https://doi.org/10.1007/978-3-030-35653-8_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35652-1

  • Online ISBN: 978-3-030-35653-8

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