Advertisement

Multi-criteria Approach to Planning of Information Spreading Processes Focused on Their Initialization with the Use of Sequential Seeding

  • Artur Karczmarczyk
  • Jarosław WątróbskiEmail author
  • Jarosław Jankowski
Conference paper
  • 15 Downloads
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 380)

Abstract

Information spreading within social networks and techniques related to viral marketing has begun to attract more interest of online marketers. While much of the prior research focuses on increasing the coverage of the viral marketing campaign, in real-life applications also other campaign goals and limitations need to be considered, such as limited time or budget, or assumed dynamics of the process. This paper presents a multi-criteria approach to planning of information spreading processes, with focus on the campaign initialization with the use of sequential seeding. A framework and example set of criteria was proposed for evaluation of viral marketing campaign strategies. The initial results showed that an increase of the count of seeding iterations and the interval between them increases the achieved coverage at the cost of increased process duration, yet without the need to increase seeding fraction or to provide incentives for increased propagation probability.

Keywords

Social networks Complex networks Viral marketing campaign planning Viral marketing campaign evaluation MCDA TOPSIS Sequential seeding 

Notes

Acknowledgments

This work was supported by the National Science Centre, Poland, grant no. 2016/21/B/HS4/01562 (AK, JJ) and within the framework of the program of the Minister of Science and Higher Education under the name “Regional Excellence Initiative” in the years 2019–2022, project number 001/RID/2018/19, the amount of financing PLN 10,684,000.00 (JW).

References

  1. 1.
    Greenwood, S., Perrin, A., Duggan, M.: Social media update 2016. Pew Res. Cent. 11(2) (2016) Google Scholar
  2. 2.
    Couldry, N.: Media, Society, World: Social Theory and Digital Media Practice. Polity Press, Cambridge (2012)Google Scholar
  3. 3.
    Chmielarz, W., Szumski, O.: Digital distribution of video games - an empirical study of game distribution platforms from the perspective of polish students (future managers). In: Ziemba, E. (ed.) AITM/ISM 2018. LNBIP, vol. 346, pp. 136–154. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-15154-6_8CrossRefGoogle Scholar
  4. 4.
    Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web 1(1), 5–44 (2007).  https://doi.org/10.1145/1232722.1232727CrossRefGoogle Scholar
  5. 5.
    Camarero, C., José, R.S.: Social and attitudinal determinants of viral marketing dynamics. Comput. Hum. Behav. 27(6), 2292–2300 (2011).  https://doi.org/10.1016/j.chb.2011.07.008CrossRefGoogle Scholar
  6. 6.
    Jankowski, J., Bródka, P., Hamari, J.: A picture is worth a thousand words: an empirical study on the influence of content visibility on diffusion processes within a virtual world. Behav. Inf. Technol. 35(11), 926–945 (2016).  https://doi.org/10.1080/0144929X.2016.1212932CrossRefGoogle 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).  https://doi.org/10.1509/jm.10.0088CrossRefGoogle Scholar
  8. 8.
    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).  https://doi.org/10.1145/1852658.1852661
  9. 9.
    Iribarren, J.L., Moro, E.: Branching dynamics of viral information spreading. Phys. Rev. E 84, 046116 (2011).  https://doi.org/10.1103/PhysRevE.84.046116CrossRefGoogle Scholar
  10. 10.
    Jankowski, J., Michalski, R., Kazienko, P.: The multidimensional study of viral campaigns as branching processes. In: Aberer, K., Flache, A., Jager, W., Liu, L., Tang, J., Guéret, C. (eds.) SocInfo 2012. LNCS, vol. 7710, pp. 462–474. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-35386-4_34CrossRefGoogle Scholar
  11. 11.
    Liu, C., Zhang, Z.K.: Information spreading on dynamic social networks. Commun. Nonlinear Sci. Numer. Simul. 19(4), 896–904 (2014).  https://doi.org/10.1016/j.cnsns.2013.08.028MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Kempe, D., Kleinberg, J., Kumar, A.: Connectivity and inference problems for temporal networks. J. Comput. Syst. Sci. 64(4), 820–842 (2002).  https://doi.org/10.1006/jcss.2002.1829MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Jankowski, J., Michalski, R., Kazienko, P.: Compensatory seeding in networks with varying avaliability of nodes. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), pp. 1242–1249. IEEE (2013).  https://doi.org/10.1145/2492517.2500256
  14. 14.
    Ganesh, A., Massoulie, L., Towsley, D.: The effect of network topology on the spread of epidemics. In: Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 1455–1466, March 2005.  https://doi.org/10.1109/INFCOM.2005.1498374
  15. 15.
    Delre, S.A., Jager, W., Bijmolt, T.H.A., Janssen, M.A.: Will it spread or not? The effects of social influences and network topology on innovation diffusion. J. Prod. Innov. Manage. 27(2), 267–282 (2010).  https://doi.org/10.1111/j.1540-5885.2010.00714.xCrossRefGoogle Scholar
  16. 16.
    Pazura, P., Jankowski, J., Bortko, K., Bartkow, P.: Increasing the diffusional characteristics of networks through optimal topology changes within sub-graphs (2019).  https://doi.org/10.1145/3341161.3344823
  17. 17.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999).  https://doi.org/10.1126/science.286.5439.509MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440 (1998).  https://doi.org/10.1038/30918CrossRefzbMATHGoogle Scholar
  19. 19.
    Erdös, P., Rényi, A.: On random graphs I. Publicationes Mathematicae Debrecen 6, 290 (1959)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Onnela, J.P., Christakis, N.A.: Spreading paths in partially observed social networks. Phys. Rev. E 85, 036106 (2012).  https://doi.org/10.1103/PhysRevE.85.036106CrossRefGoogle Scholar
  21. 21.
    Génois, M., Vestergaard, C.L., Cattuto, C., Barrat, A.: Compensating for population sampling in simulations of epidemic spread on temporal contact networks. Nat. Commun. 6, 8860 (2015).  https://doi.org/10.1038/ncomms9860CrossRefGoogle Scholar
  22. 22.
    Jankowski, J., Hamari, J., Wątróbski, J.: A gradual approach for maximising user conversion without compromising experience with high visual intensity website elements. Internet Res. 29(1), 194–217 (2019).  https://doi.org/10.1108/IntR-09-2016-0271CrossRefGoogle Scholar
  23. 23.
    Sałabun, W., Palczewski, K., Wątróbski, J.: Multicriteria approach to sustainable transport evaluation under incomplete knowledge: electric bikes case study. Sustainability 11(12), 3314 (2019).  https://doi.org/10.3390/su11123314CrossRefGoogle Scholar
  24. 24.
    Karczmarczyk, A., Wątróbski, J., Jankowski, J., Ziemba, E.: Comparative study of ICT and SIS measurement in polish households using a MCDA-based approach. Procedia Comput. Sci. 159, 2616–2628 (2019).  https://doi.org/10.1016/j.procs.2019.09.254CrossRefGoogle Scholar
  25. 25.
    Karczmarczyk, A., Jankowski, J., Wątróbski, J.: Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks. PLoS ONE 13(12), e0209372 (2018).  https://doi.org/10.1371/journal.pone.0209372CrossRefGoogle Scholar
  26. 26.
    Karczmarczyk, A., Jankowski, J., Watrobski, J.: Parametrization of spreading processes within complex networks with the use of knowledge acquired from network samples. Procedia Comput. Sci. 159, 2279–2293 (2019).  https://doi.org/10.1016/j.procs.2019.09.403CrossRefGoogle Scholar
  27. 27.
    Jankowski, J., Zioło, M., Karczmarczyk, A., Wątróbski, J.: Towards sustainability in viral marketing with user engaging supporting campaigns. Sustainability 10(1), 15 (2018).  https://doi.org/10.3390/su10010015CrossRefGoogle Scholar
  28. 28.
    Karczmarczyk, A., Jankowsk, J., Wątróbski, J.: Multi-criteria approach to viral marketing campaign planning in social networks, based on real networks, network samples and synthetic networks. In: 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 663–673. IEEE (2019). https://doi.org/10.15439/2019F199
  29. 29.
    Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 199–208. Association for Computing Machinery, New York (2009).  https://doi.org/10.1145/1557019.1557047
  30. 30.
    Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: 2010 IEEE International Conference on Data Mining, pp. 88–97, December 2010.  https://doi.org/10.1109/ICDM.2010.118
  31. 31.
    Marcinkiewicz, K., Stegmaier, M.: The parliamentary election in Poland, october 2015. Elect. Stud. 41, 221–224 (2016).  https://doi.org/10.1016/j.electstud.2016.01.004CrossRefGoogle Scholar
  32. 32.
    Enli, G.: Twitter as arena for the authentic outsider: exploring the social media campaigns of trump and clinton in the 2016 US presidential election. Eur. J. Commun. 32(1), 50–61 (2017).  https://doi.org/10.1177/0267323116682802CrossRefGoogle Scholar
  33. 33.
    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).  https://doi.org/10.1109/TNSE.2015.2425961CrossRefGoogle Scholar
  34. 34.
    Kandhway, K., Kuri, J.: How to run a campaign: optimal control of SIS and SIR information epidemics. Appl. Math. Comput. 231, 79–92 (2014).  https://doi.org/10.1016/j.amc.2013.12.164. http://www.sciencedirect.com/science/article/pii/S0096300314000022MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    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).  https://doi.org/10.1145/956750.956769
  36. 36.
    Wang, C., Chen, W., Wang, Y.: Scalable influence maximization for independent cascade model in large-scale social networks. Data Min. Knowl. Disc. 25(3), 545–576 (2012).  https://doi.org/10.1007/s10618-012-0262-1MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Kiss, C., Bichler, M.: Identification of influencers — measuring influence in customer networks. Decis. Support Syst. 46(1), 233–253 (2008).  https://doi.org/10.1016/j.dss.2008.06.007CrossRefGoogle Scholar
  38. 38.
    Seeman, L., Singer, Y.: Adaptive seeding in social networks. In: 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 459–468. IEEE (2013).  https://doi.org/10.1109/FOCS.2013.56
  39. 39.
    Kitsak, M., et al.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888 (2010).  https://doi.org/10.1038/nphys1746CrossRefGoogle Scholar
  40. 40.
    Zhang, J.X., Chen, D.B., Dong, Q., Zhao, Z.D.: Identifying a set of influential spreaders in complex networks. Sci. Rep. 6, 27823 (2016).  https://doi.org/10.1038/srep27823CrossRefGoogle Scholar
  41. 41.
    Lin, J.H., Guo, Q., Dong, W.Z., Tang, L.Y., Liu, J.G.: Identifying the node spreading influence with largest k-core values. Phys. Lett. A 378(45), 3279–3284 (2014).  https://doi.org/10.1016/j.physleta.2014.09.054CrossRefzbMATHGoogle Scholar
  42. 42.
    Ho, J.Y., Dempsey, M.: Viral marketing: motivations to forward online content. J. Bus. Res. 63(9), 1000–1006 (2010).  https://doi.org/10.1016/j.jbusres.2008.08.010CrossRefGoogle Scholar
  43. 43.
    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).  https://doi.org/10.1038/s41598-017-00937-8CrossRefGoogle Scholar
  44. 44.
    Wątróbski, J., Jankowski, J., Ziemba, P., Karczmarczyk, A., Zioło, M.: Generalised framework for multi-criteria method selection. Omega 86, 107–124 (2019).  https://doi.org/10.1016/j.omega.2018.07.004CrossRefGoogle Scholar
  45. 45.
    Wątróbski, J., Jankowski, J., Ziemba, P., Karczmarczyk, A., Zioło, M.: Generalised framework for multi-criteria method selection: rule set database and exemplary decision support system implementation blueprints. Data Brief 22, 639 (2019).  https://doi.org/10.1016/j.dib.2018.12.015CrossRefGoogle Scholar
  46. 46.
    Ripeanu, M., Foster, I., Iamnitchi, A.: Mapping the Gnutella network: properties of large-scale peer-to-peer systems and implications for system design. arXiv:cs/0209028, September 2002

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology in SzczecinSzczecinPoland
  2. 2.University of SzczecinSzczecinPoland

Personalised recommendations