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Sending Messages in Social Networks

  • Matteo CristaniEmail author
  • Francesco Olivieri
  • Claudio Tomazzoli
  • Guido Governatori
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 96)

Abstract

Since the birth of digital social networks, management research focused upon the opportunities of social media marketing. A marketing campaign has the best success when it reaches the largest number of potential customers. It is, however, difficult to forecast in a precise way the number of contacts that you can reach with such an initiative.

We propose a representation of social networks that captures both the probability of forecasting a message to different agents, and the time span during which the message is sent out.

We study reachiability and coverage from the computational complexity viewpoint and show that they can be solved polynomially on deterministic machines.

References

  1. 1.
    Bampo, M., Ewing, M.T., Mather, D.R., Stewart, D., Wallace, M.: The effects of the social structure of digital networks on viral marketing performance. Inf. Syst. Res. 19(3), 273–290 (2008)CrossRefGoogle Scholar
  2. 2.
    Cristani, M., Fogoroasi, D., Tomazzoli, C.: Measuring homophily. In: CEUR Workshop - Proceedings, vol. 1748 (2016)Google Scholar
  3. 3.
    Cristani, M., Olivieri, F., Tomazzoli, C.: Viral experiments, vol. 1959 (2017)Google Scholar
  4. 4.
    Cristani, M., Tomazzoli, C., Olivieri, F.: Semantic social network analysis foresees message flows. In: ICAART - Proceedings, vol. 1, pp. 296–303 (2016)Google Scholar
  5. 5.
    Esmaeilpour, M., Aram, F.: Investigating the impact of viral message appeal and message credibility on consumer attitude toward the brand. Manag. Mark. 11(2), 470–483 (2016)Google Scholar
  6. 6.
    Gonsalves, J.N.C., Rodrigues, H.S., Monteiro, M.T.T.: A contribution of dynamical systems theory and epidemiological modeling to a viral marketing campaign. Adv. Intell. Syst. Comput. 557, 974–983 (2017)Google Scholar
  7. 7.
    Inaltekin, H., Chiang, M., Poor, H.V.: Average message delivery time for small-world networks in the continuum limit. IEEE Trans. Inf. Theory 56(9), 4447–4470 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Khan, S.K.A., Mondragon, R.J., Tokarchuk, L.N.: Lobby influence: opportunistic forwarding algorithm based on human social relationship patterns. In: PERCOM Workshops, pp. 211–216 (2012)Google Scholar
  9. 9.
    Lu, Z., Sagduyu, Y., Shi, Y.: Friendships in the air: integrating social links into wireless network modeling, routing, and analysis. In: INFOCOM - Proceedings, vol. 2016, pp. 322–327, September 2016Google Scholar
  10. 10.
    Scott, J.: Social network analysis: developments, advances, and prospects. Soc. Netw. Anal. Min. 1(1), 21–26 (2011)CrossRefGoogle Scholar
  11. 11.
    Tomazzoli, C., Storti, S.F., Galazzo, I.B., Cristani, M., Menegaz, G.: The brain is a social network, vol. 1959 (2017)Google Scholar
  12. 12.
    Tseng, Y.-C., Ni, S.-Y., Chen, Y.-S., Sheu, J.-P.: The broadcast storm problem in a mobile ad hoc network. Wirel. Netw. 8(2–3), 153–167 (2002)CrossRefGoogle Scholar
  13. 13.
    Zhu, Y., Zhang, H., Ji, Q.: How much delay has to be tolerated in a mobile social network? Int. J. Distrib. Sens. Netw. 2013, 1–8 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Matteo Cristani
    • 1
    Email author
  • Francesco Olivieri
    • 2
  • Claudio Tomazzoli
    • 1
  • Guido Governatori
    • 2
  1. 1.Dipartimento di InformaticaUniversità di VeronaVeronaItaly
  2. 2.Data61BrisbaneAustralia

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