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

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Information Technology for Management: Current Research and Future Directions (AITM 2019, ISM 2019)

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.

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References

  1. Greenwood, S., Perrin, A., Duggan, M.: Social media update 2016. Pew Res. Cent. 11(2) (2016)

    Google Scholar 

  2. Couldry, N.: Media, Society, World: Social Theory and Digital Media Practice. Polity Press, Cambridge (2012)

    Google Scholar 

  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_8

    Chapter  Google Scholar 

  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/10.1145/1232722.1232727

    Article  Google Scholar 

  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.2011.07.008

    Article  Google Scholar 

  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.2016.1212932

    Article  Google Scholar 

  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.10.0088

    Article  Google Scholar 

  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. Iribarren, J.L., Moro, E.: Branching dynamics of viral information spreading. Phys. Rev. E 84, 046116 (2011). https://doi.org/10.1103/PhysRevE.84.046116

    Article  Google Scholar 

  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_34

    Chapter  Google Scholar 

  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.2013.08.028

    Article  MathSciNet  MATH  Google Scholar 

  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.2002.1829

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. 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.x

    Article  Google Scholar 

  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. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999). https://doi.org/10.1126/science.286.5439.509

    Article  MathSciNet  MATH  Google Scholar 

  18. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440 (1998). https://doi.org/10.1038/30918

    Article  MATH  Google Scholar 

  19. Erdös, P., Rényi, A.: On random graphs I. Publicationes Mathematicae Debrecen 6, 290 (1959)

    MathSciNet  MATH  Google Scholar 

  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.85.036106

    Article  Google Scholar 

  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/ncomms9860

    Article  Google Scholar 

  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-09-2016-0271

    Article  Google Scholar 

  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/su11123314

    Article  Google Scholar 

  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.2019.09.254

    Article  Google Scholar 

  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.0209372

    Article  Google Scholar 

  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.2019.09.403

    Article  Google Scholar 

  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/su10010015

    Article  Google Scholar 

  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. 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. 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. Marcinkiewicz, K., Stegmaier, M.: The parliamentary election in Poland, october 2015. Elect. Stud. 41, 221–224 (2016). https://doi.org/10.1016/j.electstud.2016.01.004

    Article  Google Scholar 

  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/10.1177/0267323116682802

    Article  Google Scholar 

  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.2015.2425961

    Article  Google Scholar 

  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/S0096300314000022

    Article  MathSciNet  MATH  Google Scholar 

  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. 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/s10618-012-0262-1

    Article  MathSciNet  MATH  Google Scholar 

  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.2008.06.007

    Article  Google Scholar 

  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. Kitsak, M., et al.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888 (2010). https://doi.org/10.1038/nphys1746

    Article  Google Scholar 

  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/srep27823

    Article  Google Scholar 

  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.2014.09.054

    Article  MATH  Google Scholar 

  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.2008.08.010

    Article  Google Scholar 

  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/s41598-017-00937-8

    Article  Google Scholar 

  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.2018.07.004

    Article  Google Scholar 

  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.2018.12.015

    Article  Google Scholar 

  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

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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).

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Correspondence to Jarosław Wątróbski .

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Karczmarczyk, A., Wątróbski, J., Jankowski, J. (2020). Multi-criteria Approach to Planning of Information Spreading Processes Focused on Their Initialization with the Use of Sequential Seeding. In: Ziemba, E. (eds) Information Technology for Management: Current Research and Future Directions. AITM ISM 2019 2019. Lecture Notes in Business Information Processing, vol 380. Springer, Cham. https://doi.org/10.1007/978-3-030-43353-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-43353-6_7

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