Improving Robustness of Scale-Free Networks to Message Distortion

  • Ofir Ben-Assuli
  • Arie Jacobi
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 129)


Vast numbers of organizations and individuals communicate every day by sending messages over social networks. These messages, however, are subject to change as they propagate through the network. This paper calculates the distortion of a message as it propagates in a social network with a scale-free topology, and suggests a remedial process in which a node corrects the distortion during the diffusion process to improve the robustness of scale-free networks to message distortion. We test a model on a simulation of different types of scale-free networks, and compare different sets of corrective nodes including hubs, regular (non hub) nodes, and a combination of hubs and regular nodes. Using hubs that correct the distorted message while it is diffused are shown to decrease the global error measurement of the distortion, and improve the robustness of the network.


Social networks distortion of information organizational communication scale-free networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sohn, D.: Disentangling the Effects of Social Network Density on Electronic Word-of-Mouth (eWOM) Intention. J. Comput.-Mediat. Comm. 14, 352–367 (2009)CrossRefGoogle Scholar
  2. 2.
    Strogatz, S.H.: Exploring Complex Networks. Nature 410, 268 (2001)CrossRefGoogle Scholar
  3. 3.
    Albert, R., Barabási, A.L.: Statistical Mechanics of Complex Networks. Rev. Mod. Phys. 74, 47–97 (2002)zbMATHCrossRefGoogle Scholar
  4. 4.
    Wang, X.F., Chen, G.: Complex Networks: Small-world, Scale-free and Beyond. IEEE Circ. Syst. Magazine 3, 6–20 (2003)CrossRefGoogle Scholar
  5. 5.
    Rapoport, A.: Spread of Information through a Population with Socio-Structural Bias: I. Assumption of Transitivity. B. Math. Biol. 15, 523–533 (1953a)MathSciNetGoogle Scholar
  6. 6.
    Rapoport, A.: Spread of Information through a Population with Socio-Structural Bias: II. Various Models with Partial Transitivity. B. Math. Biol. 15, 535–546 (1953b)MathSciNetGoogle Scholar
  7. 7.
    Newman, M.E.J.: Spread of Epidemic Disease on Networks. Phys. Rev. E 66, 016128 (2002)Google Scholar
  8. 8.
    Eames, K.T.D., Keeling, M.J.: Modeling Dynamic and Network Heterogeneities in the Spread of Sexually Transmitted Diseases. Proceedings of the National Academy of Sciences of the United States of America, 13330 (2002)Google Scholar
  9. 9.
    O’Reilly, C.A.: The Intentional Distortion of Information in Organizational Communication: A Laboratory and Field Investigation. Human Relations 31, 173–193 (1978)CrossRefGoogle Scholar
  10. 10.
    Panzarasa, P., Opsahl, T., Carley, M.K.: Patterns and Dynamics of Users’ Behavior and Interaction: Network Analysis of an Online Community. JASIST 60, 911–932 (2009)CrossRefGoogle Scholar
  11. 11.
    Sarker, S., Ahuja, M., Sarker, S., Kirkeby, S.: The Role of Communication and Trust in Global Virtual Teams: A Social Network Perspective. JMIS 28, 273–309 (2011)Google Scholar
  12. 12.
    Crucitti, P., Latora, V., Marchiori, M., Rapisarda, A.: Efficiency of Scale-free Networks: Error and Attack Tolerance. Physica A 320, 642 (2003)CrossRefGoogle Scholar
  13. 13.
    Latora, V., Crucitti, P.: Efficient Behavior of Small-world Networks. Phys. Rev. Lett. 87, 198701 (2001)CrossRefGoogle Scholar
  14. 14.
    Singer, Y.: Dynamic Measure of Network Robustness. In: 24th IEEE Convention on Electrical and Electronics Engineers in Israel, pp. 366–370 (2006)Google Scholar
  15. 15.
    Watts, D.J., Strogatz, S.H.: Collective Dynamics of Small-world Networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  16. 16.
    Barabási, A.L., Albert, R.: Emergence of Scaling in Random Networks. Science 286, 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Albert, R., Barabási, A.L.: Topology of Complex Networks: Local Events and Universality. Phys. Rev. Lett. 85, 5234–5237 (2000)CrossRefGoogle Scholar
  18. 18.
    Scharnhorst, A.: Complex Networks and the Web: Insights from Nonlinear Physics. J. Comput.-Mediat. Comm. 8 (2003)Google Scholar
  19. 19.
    Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabási, A.L.: Hierarchical Organization of Modularity in Metabolic Networks. Science 297, 1551–1555 (2002)CrossRefGoogle Scholar
  20. 20.
    Barabási, A.L., Crandall, R.E.: Linked: The New Science of Networks. Am. J. Phys. 71, 409 (2003)CrossRefGoogle Scholar
  21. 21.
    NWB Team: Network Workbench Tool. Indiana University and Northeastern University (2006),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ofir Ben-Assuli
    • 1
  • Arie Jacobi
    • 1
  1. 1.Ono Academic CollegeKiryat OnoIsrael

Personalised recommendations