Advertisement

The European Physical Journal Special Topics

, Volume 222, Issue 6, pp 1359–1376 | Cite as

Spreading processes on dynamically changing contact networks

  • László GulyásEmail author
  • George KampisEmail author
Regular Article Simultaneous Dynamics ON and OF Networks

Abstract

We develop and analyze an agent-based model for the study of information propagation in dynamic contact networks. We represent information as a state of a node in a network that can be probabilistically transferred to an adjacent node within a single time step. The model is based on a closed (yet sufficiently large) population that can support processes of link generation and annihilation using different contact regimes. Our study is confined to the case of homogeneous contacts, where each agent establishes and breaks contacts in the same way. We consider information to be available for spreading in a fixed time window (i.e. finite memory). We find, surprisingly, that information transmission (measured as the proportion of informed nodes after a fixed number of time steps) is identical for dynamic preferential and random networks, but radically different for the associate mixing contact regime. We also find that the probability of transmission is, similarly counterintuitively, not a main driver of the process as opposed the the main network par maters determining contact lifetime and the turnover rate on connections. We discuss the explanation and the significance of these results in the light of the fundamental difference between dynamic and static (cumulative) networks.

Keywords

European Physical Journal Special Topic Dynamic Network Preferential Attachment Contact Network Causal Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M. Newman, Networks: an introduction (Oxford University Press, Inc., 2010)Google Scholar
  2. 2.
    S.N. Dorogovtsev, Lectures on complex networks (Oxford University Press, 2010)Google Scholar
  3. 3.
    D. Easley, J. Kleinberg, Networks, crowds, and markets (Cambridge University Press, 2010)Google Scholar
  4. 4.
    R. Cohen, S. Havlin, Complex networks: structure, robustness and function (Cambridge University Press, 2010)Google Scholar
  5. 5.
    B. Bollobás, R. Kozma, D. Miklos, Handbook of large-scale random networks, Vol. 18 (Springer, 2009)Google Scholar
  6. 6.
    P.S. Bearman, J. Moody, K. Stovel, Amer. J. Sociol. 110, 44 (2004)CrossRefGoogle Scholar
  7. 7.
    L. Gulyás, S. Khor, R. Legéndi, G. Kampis, Elementary dynamic networks. In Sunbelt XXXI, International Sunbelt Social Network Conference, St. Pete Beach, FL (2011)Google Scholar
  8. 8.
    L. Isella, J. Stehlé, A. Barrat, C. Cattuto, J.F. Pinton, W. Van den Broeck, J. Theoretical Biol. 271, 166 (2011)CrossRefGoogle Scholar
  9. 9.
    L. Isella, M. Romano, A. Barrat, C. Cattuto, V. Colizza, W. Van den Broeck, F. Gesualdo, E. Pandolfi, L. Ravà, C. Rizzo, et al., PLoS One 6, e17144 (2011)ADSCrossRefGoogle Scholar
  10. 10.
    M. Morris, H. Epstein, M. Wawer, PLoS One 5, e14092 (2010)ADSCrossRefGoogle Scholar
  11. 11.
    M. Morris, A.E. Kurth, D.T. Hamilton, J. Moody, S. Wakefield, Amer. J. Public Health 99, 1023 (2009)CrossRefGoogle Scholar
  12. 12.
    M.J. Keeling, K.T.D. Eames, J. Royal Soc. Interf. 2, 295 (2005)CrossRefGoogle Scholar
  13. 13.
    R.A. Stein, Int. J. Infect. Diseases 15, e510 (2011)CrossRefGoogle Scholar
  14. 14.
    P.M.A. Sloot, S.V. Ivanov, A.V. Boukhanovsky, D.A.M.C. Van De Vijver, C.A.B. Boucher, Int. J. Computer Math. 85, 1175 (2008)CrossRefzbMATHGoogle Scholar
  15. 15.
    S. Mei, P.M.A. Sloot, R. Quax, Y. Zhu, W. Wang, Math. Computers Simul. 80, 1018 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    R.O. Legendi, L. Gulyas, Effects of time-dependent edge dynamics on properties of cumulative networks, EPNACS-Emergent Properties in Natural and Artificial Complex Systems (2011)Google Scholar
  17. 17.
    L. Gulyás, G. Kampis, R. Legéndi, Eur. Phys. J. Special Topics 222 (6), 1311 (2013)CrossRefADSGoogle Scholar
  18. 18.
    G. Kampis, L. Gulyás, NetSci 2010 The International School and Conference on Network Science, 10 (2010)Google Scholar
  19. 19.
    R.K. Merton, Science 159, 56 (1968)ADSCrossRefGoogle Scholar
  20. 20.
    P.L. Krapivsky, S. Redner, Phys. Review E 63, 066123 (2001)ADSCrossRefGoogle Scholar
  21. 21.
    P.L. Krapivsky, S. Redner, Stat. Mech. Complex Networks, 3 (2003)Google Scholar
  22. 22.
    R. Albert, A.L. Barabási, Rev. Modern Phys. 74, 47 (2002)MathSciNetADSCrossRefzbMATHGoogle Scholar
  23. 23.
    A.L. Barabási, R. Albert, Science 286, 509 (1999)MathSciNetADSCrossRefGoogle Scholar
  24. 24.
    P. Erdos, A. Rényi, Publ. Math. 6, 290 (1959)Google Scholar
  25. 25.
    P. Erdos, A. Rényi, Magyar Tud. Akad. Mat. Kutató Int. Közl 5, 17 (1960)Google Scholar
  26. 26.
    B. Bollobás, Random graphs, Vol. 73 (Cambridge University Press, 2001)Google Scholar
  27. 27.
    M.E.J. Newman, Phys. Rev. Lett. 89, 208701 (2002)ADSCrossRefGoogle Scholar
  28. 28.
    M. McPherson, L. Smith-Lovin, J.M. Cook, Ann. Rev. Sociol. 415 (2001)Google Scholar
  29. 29.
    S.M. Goodreau, J.A. Kitts, M. Morris, Demography 46, 103 (2009)CrossRefGoogle Scholar
  30. 30.
    M. Girvan, M.E.J. Newman, Proc. National Acad. Sci. 99, 7821 (2002)MathSciNetADSCrossRefzbMATHGoogle Scholar
  31. 31.
    U. Wilensky, Netlogo, http://ccl.northwestern.edu/netlogo. Evanston, IL: Northwestern university. Center for Connected Learning and Computer-Based Modeling (1999)
  32. 32.
    T. Máhr, R. Bocsi, L. Gulyás, Simulation as a service: The model exploration service. In 3rd World Congress on Social Simulation, Kassel, Germany (2010)Google Scholar
  33. 33.
    M. Iványi, L. Gulyás, R. Bocsi, G. Szemes, R. Mészáros, Model exporation module. In Agent 2007: Complex Interaction and Social Emergence Conference, Evanston, IL, November 15–18 (2010)Google Scholar
  34. 34.
    L. Gulyás, A. Szabó, R. Legéndi, T. Máhr, R. Bocsi, G. Kampis, Tools for large scale (distributed) agent-based computational experiments. 2011 Computational Social Science Society of America Annual Conference, Santa Fe, NM, USA (2012)Google Scholar
  35. 35.
    F. Liljeros, C.R. Edling, L.A.N.A Amaral, E.H. Stanley, Y. Åberg, Nature 411, 907 (2001)ADSCrossRefGoogle Scholar
  36. 36.
    N. Zarrabi, M. Prosperi, R.G. Belleman, M. Colafigli, A. De Luca, P.M.A. Sloot, PloS one 7, e46156 (2012)ADSCrossRefGoogle Scholar

Copyright information

© EDP Sciences and Springer 2013

Authors and Affiliations

  1. 1.PetaByte LtdBudapestHungary
  2. 2.AITIA International, IncBudapestHungary
  3. 3.Eötvös Loránd UniversityBudapestHungary
  4. 4.DFKI (German Research Institute for Artificial Intelligence)KaiserslauternGermany

Personalised recommendations