Abstract
Social network analysis is a tool to assess social interactions between people e.g. in the Internet. One of the most active areas in this field are modeling influence of users and finding influential users. These areas have many applications, e.g., in marketing, business or politics. Several models of influence have been described in literature, but there is no single model that best describes the process of spreading entities (e.g. information, behaviour) through the network. Interesting and practical problem is how to choose a small number of users that will guarantee maximal spread of entities over the whole network (influence maximization problem). In this paper we studied this problem using various centrality metrics with different models of influence propagation. Experiments were conducted on three, real-world datasets regarding the domain of recommendation services.
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References
Bhagat, S., Goyal, A., Lakshmanan, L.V.: Maximizing product adoption in social networks. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 603–612. ACM, New York (2012)
Chen, W., Collins, A., Cummings, R., Ke, T., Liu, Z., Rincón, D., Sun, X., Wang, Y., Wei, W., Yuan, Y.: Influence maximization in social networks when negative opinions may emerge and propagate. In: Proceedings of the Eleventh International Conference on Data Mining, SDM 2011, Mesa, Arizona, USA, 28–30 April 2011, pp. 379–390 (2011)
Chen, Y.C., Peng, W.C., Lee, S.Y.: Efficient algorithms for influence maximization in social networks. Knowl. Inf. Syst. 33(3), 577–601 (2012). http://dx.org/10.1007/s10115-012-0540-7
Eccleston, D., Griseri, L.: How does web 2.0 stretch traditional influencing patterns. Int. J. Mark. Res. 50(5), 591–616 (2008)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)
Godes, D., Mayzlin, D.: Firm-created word-of-mouth communication: evidence from a field test. Mark. Sci. 28(4), 721–739 (2009)
Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: A data-based approach to social influence maximization. Proc. VLDB Endow. 5(1), 73–84 (2011)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 135–142. ACM, New York (2010)
Katz, E., Lazarsfeld, P.: Personal Influence: The Part Played by People in the Flow of Mass Communications. Transaction Publishers, New Brunswick (2005)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)
Kermack, W.O., McKendrick, A.G.: Contributions to the mathematical theory of epidemics-II. The problem of endemicity. Proc. Roy. Soc. London Math. Phys. Eng. Sci. 138(834), 55–83 (1932)
Kundu, S., Murthy, C.A., Pal, S.K.: A new centrality measure for influence maximization in social networks. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds.) PReMI 2011. LNCS, vol. 6744, pp. 242–247. Springer, Heidelberg (2011)
Leavitt, H.J.: Some effects of certain communication patterns on group performance. J. Abnorm. Soc. Psychol. 46(1), 38 (1951)
Lovasz, L.: Random walks on graphs: A survey (1993)
Narayanam, R., Narahari, Y.: A shapley value-based approach to discover influential nodes in social networks. IEEE Trans. Autom. Sci. Eng. 8(1), 130–147 (2011)
Niemczura, B., Gliwa, B., Zygmunt, A.: Linear threshold behavioral model for the spread of influence in recommendation services. In: The Second European Network Intelligence Conference, ENIC 2015, pp. 98–105 (2015)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web (1999)
Zafarani, R., Abbasi, M.A., Liu, H.: Influence and homophily. In: Zafarani, R., Abbasi, M.A., Liu, H. (eds.) Social Media Mining. Cambridge University Press (2014)
Ryan, B., Gross, N.C.: Acceptance and diffusion of hybrid corn seed in two Iowa communities, vol. 372. Agricultural Experiment Station, Iowa State College of Agriculture and Mechanic Arts (1950)
Shaw, M.E.: Group structure and the behavior of individuals in small groups. J. Psychol. 38(1), 139–149 (1954)
Wei, L., Laks, V.S.: Profit maximization over social networks. CoRR abs/1210.4211 (2012)
Zhuang, H., Sun, Y., Tang, J., Zhang, J., Sun, X.: Influence maximization in dynamic social networks. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 1313–1318, December 2013
Acknowledgements
The research reported in the paper was partially supported by the grant No. DOBR-BIO4/060/13423/2013 from the Polish National Centre for Research and Development.
The authors thank to Bartosz Niemczura, a student of Computer Science of AGH-UST, for his collaboration.
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Gliwa, B., Zygmunt, A. (2016). Methods for Selecting Nodes for Maximal Spread of Influence in Recommendation Services. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_8
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