Increasing Coverage of Information Spreading in Social Networks with Supporting Seeding

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10387)

Abstract

Campaigns based on information spreading processes within online networks have become a key feature of marketing landscapes. Most research in the field has concentrated on propagation models and improving seeding strategies as a way to increase coverage. Proponents of such research usually assume selection of seed set and the initialization of the process without any additional support in following stages. The approach presented in this paper shows how initiation by seed set process can be supported by selection and activation of additional nodes within network. The relationship between the number of additional activations and the size of initial seed set is dependent on network structures and propagation parameters with the highest performance observed for networks with low average degree and smallest propagation probability in a chosen model.

Keywords

Information spreading Supporting seeding Viral marketing Word of mouth Complex networks 

References

  1. 1.
    Bello-Orgaz, G., Jung, J.J., Camacho, D.: Social big data: recent achievements and new challenges. Inf. Fusion 28, 45–59 (2016)CrossRefGoogle Scholar
  2. 2.
    Berger, J., Milkman, K.L.: What makes online content viral? J. Mark. Res. 49(2), 192–205 (2012)CrossRefGoogle Scholar
  3. 3.
    Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1029–1038. ACM (2010)Google Scholar
  4. 4.
    Granell, C., Gómez, S., Arenas, A.: Competing spreading processes on multiplex networks: awareness and epidemics. Phys. Rev. E 90(1), 012808 (2014)CrossRefGoogle Scholar
  5. 5.
    Hanna, R., Rohm, A., Crittenden, V.L.: We’re all connected: the power of the social media ecosystem. Bus. Horiz. 54(3), 265–273 (2011)CrossRefGoogle Scholar
  6. 6.
    He, J.-L., Fu, Y., Chen, D.-B.: A novel top-k strategy for influence maximization in complex networks with community structure. PLoS ONE 10, e0145283 (2015)CrossRefGoogle Scholar
  7. 7.
    Hinz, O., Skiera, B., Barrot, C., Becker, J.U.: Seeding strategies for viral marketing: an empirical comparison. J. Mark. 75(6), 55–71 (2011)CrossRefGoogle Scholar
  8. 8.
    Ho, J.Y., Dempsey, M.: Viral marketing: motivations to forward online content. J. Bus. Res. 63(9), 1000–1006 (2010)CrossRefGoogle Scholar
  9. 9.
    Iribarren, J.L., Moro, E.: Impact of human activity patterns on the dynamics of information diffusion. Phys. Rev. Lett. 103(3), 038702 (2009)CrossRefGoogle Scholar
  10. 10.
    Jankowski, J., Bródka, P., Kazienko, P., Szymanski, B.K., Michalski, R., Kajdanowicz, T.: Balancing speed and coverage by sequential seeding in complex networks. Sci. Rep. 7(1), 891 (2017)CrossRefGoogle Scholar
  11. 11.
    Jankowski, J.: Dynamic rankings for seed selection in complex networks: balancing costs and coverage. Entropy 19(4), 170 (2017)CrossRefGoogle Scholar
  12. 12.
    Joshi-Tope, G., et al.: Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 33, D428–D432 (2005)CrossRefGoogle Scholar
  13. 13.
    Kandhway, K., Kuri, J.: How to run a campaign: optimal control of SIS and SIR information epidemics. Appl. Math. Comput. 231, 79–92 (2014)MathSciNetGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)CrossRefGoogle Scholar
  16. 16.
    Leskovec J., Kleinberg J., Faloutsos C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 177–187. ACM (2005)Google Scholar
  17. 17.
    Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In Advances in Neural Information Processing Systems, pp. 539–547 (2012)Google Scholar
  18. 18.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: densification and shrinking diameters. ACM Trans. Knowl. Discov. Data 1(1), 2 (2007)CrossRefGoogle Scholar
  19. 19.
    Ley, M.: The DBLP computer science bibliography: evolution, research issues, perspectives. In: International Symposium on String Processing and Information Retrieval, pp. 1–10 (2002)Google Scholar
  20. 20.
    Michalski, R., Kajdanowicz, T., Bródka, P., Kazienko, P.: Seed selection for spread of influence in social networks: temporal vs. static approach. New Gener. Comput. 32(3–4), 213–235 (2014)CrossRefGoogle Scholar
  21. 21.
    Newman, M.E.: Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. E 64, 016131 (2001)CrossRefGoogle Scholar
  22. 22.
    Opsahl, T., Panzarasa, P.: Clustering in weighted networks. Soc. Netw. 31, 155–163 (2009)CrossRefGoogle Scholar
  23. 23.
    Pfitzner, R., Garas, A., Schweitzer, F.: Emotional divergence influences information spreading in twitter. In: Proceedings of Sixth International Conference on Weblogs and Social Media, pp. 2–5 (2012)Google Scholar
  24. 24.
    Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2010)Google Scholar
  25. 25.
    Salehi, M., Sharma, R., Marzolla, M., Magnani, M., Siyari, P., Montesi, D.: Spreading processes in multilayer networks. IEEE Trans. Netw. Sci. Eng. 2(2), 65–83 (2015)CrossRefGoogle Scholar
  26. 26.
    Seeman, L., Singer, Y.: Adaptive seeding in social networks. In Foundations of Computer Science (FOCS), IEEE 54th Annual Symposium, pp. 459–468. IEEE (2013)Google Scholar
  27. 27.
    Subelj, L. Bajec, M.: Software systems through complex networks science: Review, analysis and applications. In: Proceedings of the First International Workshop on Software Mining, pp. 9–16. ACM (2012)Google Scholar
  28. 28.
    Tang, J., Musolesi, M., Mascolo, C., Latora, V., Nicosia, V.: Analysing information flows and key mediators through temporal centrality metrics. In: Proceedings of the 3rd Workshop on Social Network Systems, p. 3. ACM (2010)Google Scholar
  29. 29.
    Watts, D.J., Peretti, J., Frumin, M.: Viral Marketing for the Real World. Harvard Business School Pub, Boston (2007)Google Scholar
  30. 30.
    Zhang, J.-X., Duan-Bing Chen, Q.D., Zhao, Z.-D.: Identifying a set of influential spreaders in complex networks. Sci. Rep. 6 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland
  2. 2.Department of Computational IntelligenceWrocław University of Science and TechnologyWrocławPoland

Personalised recommendations