Advertisement

Identifying Influential Spreaders in Complex Networks with Probabilistic Links

  • Pavlos Basaras
  • Dimitrios KatsarosEmail author
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

Dynamic complex networks illustrate how “agents” interact by exchanging information in a constantly changing network. Typical examples of such networks are online social networks or human contacts. This article contemplates the common distribution of time that user-nodes spend on their activities, and describes a method for identifying real-time influential spreaders. We model the reciprocal activities of actor-nodes with probabilistic links and propose a technique for identifying influential spreaders in complex networks with probabilistic edges. The proposed measure, namely, ranged Probabilistic Communication Area (rPCA), is evaluated under the susceptible-infectious-removed (SIR) model, where the results illustrate that rPCA can detect very effective spreaders in a networked environment with probabilistic edges.

Notes

Acknowledgements

This work was supported by the project “REDUCTION: Reducing Environ-mental Footprint based on Multi-Modal Fleet management System for Eco-Routing and Driver Behaviour Adaptation”, funded by the EU.ICT program, Challenge ICT-2011.7.

References

  1. 1.
    K. Weil, Measuring tweets (2010). Twitter Official Blog, February 22Google Scholar
  2. 2.
    R. Krikorian, New tweets per second record, and how! (2013). Twitter Official Blog, August 16Google Scholar
  3. 3.
    T. Smieszek, Theoretical Biology and Medical Modelling 6, 2–15 (2009)CrossRefGoogle Scholar
  4. 4.
    P. Basaras, D. Katsaros, L. Tassiulas, IEEE Comput. Mag. 46(4), 26 (2013)CrossRefGoogle Scholar
  5. 5.
    B. Joonhyun, K. Sangwook, Physica A 395(1), 549 (2014)Google Scholar
  6. 6.
    M. Kitsak, L.K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H.E. Stanley, H.A. Makse, Nat. Phys. 6, 888 (2010)CrossRefGoogle Scholar
  7. 7.
    A.L. Barabasi, Nature 435(6), 207 (2005)CrossRefGoogle Scholar
  8. 8.
    J.B. Holthoefer, S. Meloni, B. Goncalves, Y. Moreno, J. Stat. Phys. 151, 383 (2013)CrossRefGoogle Scholar
  9. 9.
    J.B. Holthoefer, A. Rivero, Y. Moreno, Phys. Rev. E 85, 066123:1 (2012)Google Scholar
  10. 10.
    P. Domingos, M. Richardson, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2002), pp. 57–66Google Scholar
  11. 11.
    D. Kempe, J. Kleinberg, E. Tardos, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2003), pp. 137–146Google Scholar
  12. 12.
    J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J.M. van Briesen, N.S. Glance, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2007), pp. 420–429Google Scholar
  13. 13.
    C.T. Li, T.T. Kuo, C.T. Ho, S.C. Hong, W.S. Lin, S.D. Lin, Soc. Netw. Anal. Min. 3(3), 341 (2013)CrossRefGoogle Scholar
  14. 14.
    N. Kourtellis, T. Alahakoon, R. Simha, A. Iamnitchi, R. Tripathi, Soc. Netw. Anal. Min. 3(4), 899 (2013)CrossRefGoogle Scholar
  15. 15.
    B. Han, A. Srinivasan, Proceedings of the ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC) (2012), pp. 5–14Google Scholar
  16. 16.
    J.G. Liu, Z.M. Ren, Q. Guo, Physica A 392(18), 4154 (2013)CrossRefGoogle Scholar
  17. 17.
    A. Zeng, C.J. Zhang, Phys. Lett. A 377(14), 1031 (2013)CrossRefGoogle Scholar
  18. 18.
    D. Chen, L. Lu, M.S. Shang, Y.C. Zhang, T. Zhou, Physica A 391(4), 1777 (2012)CrossRefGoogle Scholar
  19. 19.
    S. Boccaletti, G. Bianconi, R. Criado, C.I. del Genio, J. Gomez-Gardenes, M. Romance, I. Sendina-Nadal, Z. Wang, M. Zanin, Phys. Rep. 544, 1 (2014)CrossRefGoogle Scholar
  20. 20.
    M. Salehi, R. Sharma, M. Marzolla, M. Magnani, P. Siyari, D. Montesi, IEEE Trans. Netw. Sci. Eng. 2(2), 65 (2015)CrossRefGoogle Scholar
  21. 21.
    M.A. Al-garadi, K.D. Varathan, S.D. Ravana, E. Ahmed, V. Chang, J. Intell. Fuzzy Syst. 31(5), 2721 (2016)CrossRefGoogle Scholar
  22. 22.
    N. Azimi-Tafreshi, J. Gomez-Gardenes, S.N. Dorogovtsev, Phys. Rev. E 90(3), 032816 (2014)CrossRefGoogle Scholar
  23. 23.
    Z. Dawei, L. Lixiang, L. Shudong, H. Yujia, Y. Yixian, Phys. Scr. 89(1), 015203 (2014)CrossRefGoogle Scholar
  24. 24.
    M. De Domenico, A. Solé-Ribalta, E. Omodei, S. Gómez, A. Arenas, Nat. Commun. 6, 6868 (2015)CrossRefGoogle Scholar
  25. 25.
    B. Joonhyun, K. Sangwook, Physica A 395, 549 (2014)CrossRefGoogle Scholar
  26. 26.
    S. Brin, L. Page, Comput. Netw. ISDN Syst. 30(1–7), 107 (1998)CrossRefGoogle Scholar
  27. 27.
    L. Lu, Y.C. Zhang, C.H. Yeung, T. Zhou, PLoS ONE 6, 0021202:1 (2011)Google Scholar
  28. 28.
    Q. Li, T. Zhou, L. Lv, D. Chen, Physica A 404, 47 (2014)CrossRefGoogle Scholar
  29. 29.
    J. Weng, E.P. Lim, J. Jang, Q. He, Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM) (2010), pp. 261–270Google Scholar
  30. 30.
    P. Holme, Proc. IEEE 102(12), 1922 (2014)CrossRefGoogle Scholar
  31. 31.
    F. Menczer, Proc. Natl. Acad. Sci. 99(22), 14014 (2002)CrossRefGoogle Scholar
  32. 32.
    R. Baeza-Yates, E. Davis, Proceedings of the ACM International World Wide Web Conference (WWW) (2004), pp. 328–329Google Scholar
  33. 33.
    M. Eidsaa, E. Almaas, Phys. Rev. E 88, 062819:1 (2013)Google Scholar
  34. 34.
    V. Gemmetto, C. Barrat, A. Cattuto, BMC Infect. Dis. 14, 694:1 (2014)Google Scholar
  35. 35.
    J. Leskovec, A. Krevl, SNAP datasets: Stanford large network dataset collection (2014). http://snap.stanford.edu/data
  36. 36.
    B.A. Prakash, D. Chakrabarti, N.C. Valler, M. Faloutsos, C. Faloutos, Knowl. Inf. Syst. 33(3), 549 (2012)CrossRefGoogle Scholar
  37. 37.
    L. Cong, H. Wang, P.V. Mieghem, Phys. Rev. E 88, 062802:1 (2013)Google Scholar
  38. 38.
    M. Kendall, Biometrika 30, 81 (1938)CrossRefGoogle Scholar
  39. 39.
    P. Devi, A. Gupta, A. Dixit, Int. J. Adv. Res. Comput. Commun. Eng. 3(2), 5749 (2014)Google Scholar
  40. 40.
    J.K. Kleinberg, J. ACM 46(5), 604 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of ThessalyVolosGreece

Personalised recommendations