Social Network Analysis and Mining

, Volume 3, Issue 4, pp 1195–1208 | Cite as

Inadequacy of SIR model to reproduce key properties of real-world spreading cascades: experiments on a large-scale P2P system

  • Daniel F. BernardesEmail author
  • Matthieu Latapy
  • Fabien Tarissan
Original Article


Understanding the spread of information on complex networks is a key issue from a theoretical and applied perspective. Despite the effort in developing theoretical models for this phenomenon, gauging them with large-scale real-world data remains an important challenge due to the scarcity of open, extensive and detailed data. In this paper, we explain how traces of peer-to-peer file sharing may be used to reach this goal. We reconstruct the underlying social network of peers sharing content and perform simulations on it to assess the relevance of the standard SIR model to mimic key properties of real spreading cascades. First, we examine the impact of the network topology on observed properties. Then we turn to the evaluation of two heterogeneous extensions of the SIR model. Finally, we improve the social network reconstruction, introducing an affinity index between peers, and simulate a SIR model which integrates this new feature. We conclude that the simple, homogeneous model is insufficient to mimic real spreading cascades. Moreover, none of the natural extensions of the model we considered, which take into account extra topological properties, yielded satisfying results in our context. This raises an alert against the careless, widespread use of this model.


Bipartite Graph Random Graph Degree Distribution File Sharing Infected Node 
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.



This work is partly funded by the European Commission through the FP7-FIRE project EULER (Grant No.258307) and by the City of Paris Emergence program through the DiRe project.


  1. Andersson H, Britton T (2000) Stochastic epidemic models and their statistical analysis (Lecture Notes in Statistics), vol 151, 1st edn. Springer, BerlinGoogle Scholar
  2. Anderson R, May R (1991) Infectious diseases of humans: dynamics and control. Science Publications, OxfordGoogle Scholar
  3. Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83(6): 1420–1443Google Scholar
  4. Easley DA, Kleinberg JM (2010) Networks, crowds, and markets—reasoning about a highly connected world. Cambridge University Press, New YorkGoogle Scholar
  5. Jackson MO (2008) Social and economic networks. Princeton University Press, PrincetonGoogle Scholar
  6. Barrat A, Barthlemy M, Vespignani A (2008) Dynamical processes on complex networks. Cambridge University Press, New YorkGoogle Scholar
  7. Draief M, Massoulié L (2010) Epidemics and rumours in complex networks, ser. London Mathematical Society lecture note series, no. 369. Cambridge University Press, New YorkGoogle Scholar
  8. Newman MEJ (2003) The structure and function of complex networks. SIAM REVIEW 45:167–256Google Scholar
  9. Prakash BA, Chakrabarti D, Faloutsos M, Valler N, Faloutsos C (2011) Threshold conditions for arbitrary cascade models on arbitrary networks. In: 2011 IEEE 11th International Conference on Data Mining, Vancouver, BC, pp 537–546Google Scholar
  10. Hosanagar K, Han P, Tan Y (2010) Diffusion models for peer-to-peer (P2P) media distribution: on the impact of decentralized, constrained supply. Info Sys Res 21(2):271–287CrossRefGoogle Scholar
  11. Leibnitz K, Hossfeld T, Wakamiya N, Murata M (2006) Modeling of epidemic diffusion in peer-to-peer file-sharing networks, in Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology, ser. BioADIT’06. Springer, Berlin, pp 322–329Google Scholar
  12. Colizza V, Barrat A, Barthélemy M, Vespignani A (2006) The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Natl Acad Sci USA 103(7):2015–2020Google Scholar
  13. Cointet J-P, Roth C (2007) How realistic should knowledge diffusion models be? J Artif Soc Soc Simul 10(3):5Google Scholar
  14. Leskovec J, McGlohon M, Faloutsos C, Glance N, Hurst M (2007) Cascading behavior in large blog graphs. In: Proceedings of 7th SIAM International Conference on Data Mining (SDM), pp 29406–29413Google Scholar
  15. Adar E, Zhang L, Adamic LA, Lukose RM (2004) Implicit structure and the dynamics of blogspace, in World Wide Web Conference SeriesGoogle Scholar
  16. Iribarren JL, Moro E (2009) Impact of human activity patterns on the dynamics of information diffusion. Phys Rev Lett 103(3):038702Google Scholar
  17. Cha M, Pérez J, Haddadi H (2012) The spread of media content through blogs. Soc Netw Anal Min 2(3):249–264CrossRefGoogle Scholar
  18. Gomez-Rodriguez M, Leskovec J, Krause A (2012) Inferring networks of diffusion and influence. ACM Trans Knowl Discov Data 5(4):21:1–21:37Google Scholar
  19. Bernardes DF, Latapy M, Tarissan F (2012) Relevance of sir model for real-world spreading phenomena: Experiments on a large-scale p2p system, in Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2012, istanbul, Turkey; 2012-08-26 – 2012-08-29Google Scholar
  20. Aidouni F, Latapy M, Magnien C (2009) Ten weeks in the life of an edonkey server, in 23rd IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2009, Rome, Italy, May 23–29, 2009, pp 1–5Google Scholar
  21. Adar E, Huberman B (2000) Free riding on gnutella. First Monday, vol. 5, no. 10-2Google Scholar
  22. Handurukande SB, Kermarrec A-M, Le Fessant F, Massoulié L, Patarin S (2006) Peer sharing behaviour in the edonkey network, and implications for the design of server-less file sharing systems, in Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006, ser. EuroSys ’06. ACM, New York, pp 359–371Google Scholar
  23. Latapy M, Magnien C, Vecchio ND (2008) Basic notions for the analysis of large two-mode networks. Soc Netw 30(1):31–48Google Scholar
  24. Iamnitchi A, Ripeanu M, Santos-Neto E, Foster I (2011) The small world of file sharing, IEEE Trans Parallel Distrib Syst 22(7):1120–1134CrossRefGoogle Scholar
  25. Allali O, Tabourier L, Magnien C, Latapy M (2013) Internal links and pairs as a new tool for the analysis of bipartite complex networks. Soci Netw Anal Min 3(1):85–91CrossRefGoogle Scholar
  26. Barrat A, Barthlemy M, Vespignani A (2008) Dynamical processes on complex networks. Cambridge University Press, New YorkGoogle Scholar
  27. Sencan H, Chen Z, Hendrix W, Pansombut T, Semazzi FHM, Choudhary AN, Kumar V, Melechko AV, Samatova NF (2011) Classification of emerging extreme event tracks in multivariate spatio-temporal physical systems using dynamic network structures: application to hurricane track prediction, in IJCAI, pp 1478–1484Google Scholar
  28. Guillaume J-L, Latapy M (2004) Bipartite structure of all complex networks. Inf Proc Lett 90(5):215–221MathSciNetCrossRefzbMATHGoogle Scholar
  29. Onnela J-P, Saramäki J, Hyvönen J, Szabó G, Lazer D, Kaski K, Kertész J, Barabási A-L (2007) Structure and tie strengths in mobile communication networks. Proc Natl Acad Sci 104(18):7332–7336Google Scholar
  30. Liben-Nowell D, Kleinberg J (2008) Tracing information flow on a global scale using internet chain-letter data. Proc Natl Acad Sci 105(12):4633–4638CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Daniel F. Bernardes
    • 1
    Email author
  • Matthieu Latapy
    • 1
  • Fabien Tarissan
    • 1
  1. 1.LIP6, CNRS and Université Pierre et Marie Curie/Paris 6Paris Cedex 05France

Personalised recommendations