Effects of Inoculation Based on Structural Centrality on Rumor Dynamics in Social Networks

  • Anurag Singh
  • Rahul Kumar
  • Yatindra Nath Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7936)


In social networks, the mechanism to suppress harmful rumors is of great importance. A rumor spreading model has been defined using the susceptible-infected-refractory (SIR) model to characterize rumor propagation in social networks. In this paper a new inoculation strategy based on structural centrality has been applied on rumor spreading model for heterogeneous networks. It is compared with the targeted and random inoculations. The structural centrality of each nodes has been ranked in the topology of social networks which is modeled as scale free network. The nodes with higher structural centrality are chosen for inoculation in the proposed strategy. The structural centrality based inoculation strategy is more efficient in comparison with the random and targeted inoculation strategies. One of the bottlenecks is the high complexity to calculate the structural centrality of the nodes for very large number of nodes in the complex networks. The proposed hypothesis has been verified using simulation results for email network data and the generated scale free networks.


Complex networks graph spectra rumor spreading model inoculation strategies 


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  1. 1.
    Moreno, Y., Pastor-Satorras, R., Vespignani, A.: Epidemic outbreaks in complex heterogeneous networks. Euro. Phy, J. B 26(4), 521–529 (2002)Google Scholar
  2. 2.
    Zhou, J., Xiao, G., Cheong, S.A., Fu, X., Wong, L., Ma, S., Cheng, T.H.: Epidemic reemergence in adaptive complex networks. Phys. Rev. E 85, 036107 (2012)CrossRefGoogle Scholar
  3. 3.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Newman, M.: The structure and function of complex networks. Siam Review 45(2), 167–256 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Daley, D., Gani, J., Gani, J.: Epidemic Modelling: An Introduction. Cambridge University Press, Cambridge (2001)zbMATHGoogle Scholar
  6. 6.
    Maki, D., Thompson, M.: Mathematical models and applications: with emphasis on the social, life, and management sciences. Prentice-Hall, NJ (1973)Google Scholar
  7. 7.
    Nekovee, M., Moreno, Y., Bianconi, G., Marsili, M.: Theory of rumor spreading in complex social networks. Phy. A 374(1), 457–470 (2007)CrossRefGoogle Scholar
  8. 8.
    Singh, A., Singh, Y.N.: Nonlinear spread of rumor and inoculation strategies in the nodes with degree dependent tie stregth in complex networks. Acta Physica Polonica B 44(1), 5–28 (2013)CrossRefGoogle Scholar
  9. 9.
    Singh, A., Singh, Y.N.: Rumor spreading and inoculation of nodes in complex networks. In: Proceedings of the 21st International Conference Companion on World Wide Web, WWW 2012 Companion, pp. 675–678. ACM (2012)Google Scholar
  10. 10.
    Singh, A., Kumar, R., Singh, Y.N.: Rumor dynamics with acceptability factor and inoculation of nodes in scale free networks. In: 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS), pp. 798–804 (November 2012)Google Scholar
  11. 11.
    Singh, A., Singh, Y.N.: Rumor dynamics with inoculations for correlated scale free networks. In: 2013 National Conference on Communications (NCC), pp. 1–5 (2013)Google Scholar
  12. 12.
    Pastor-Satorras, R., Vespignani, A.: Epidemics and immunization in scale-free networks. In: Bornholdt, S., Schuster, H.G. (eds.) Handbook of Graphs and Networks: From the Genome to the Internet, pp. 113–132. Wiley-VCH, Berlin (2002)Google Scholar
  13. 13.
    Spielman, D.: Spectral graph theory and its applications. In: 48th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2007, pp. 29–38 (October 2007)Google Scholar
  14. 14.
    Freeman, L.: Centrality in social networks conceptual clarification. Social Networks 1(3), 215–239 (1979)CrossRefGoogle Scholar
  15. 15.
    Fouss, F., Pirotte, A., Renders, J.M., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering 19(3), 355–369 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Anurag Singh
    • 1
  • Rahul Kumar
    • 1
  • Yatindra Nath Singh
    • 1
  1. 1.Electrical Engineering DepartmentIIT KanpurIndia

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