Structure and Evolution of Online Social Networks



In this work, we consider the evolution of structure within large online social networks. We present a series of measurements of two large real networks, one from the friend relation within the Flickr photo sharing application and the other from Yahoo!s 360 social network. These networks together comprise in excess of 5 million people and 10 million friendship links, and they are annotated with metadata capturing the time of every event in the life of the network. We show that these networks may be segmented into three regions: singletons, who do not participate in the network, isolated communities, which overwhelmingly display star structure, and a giant component anchored by a well-connected core region that persists even in the absence of stars. We give a detailed characterization of the structure and evolution of these regions. We also present a simple model of network growth that captures these aspects of component structure. The model follows our experimental results, characterizing users as either passive members of the network, inviters who encourage offline friends and acquaintances to migrate online, and linkers who fully participate in the social evolution of the network. We show that this simple model with only two numerical parameters is able to produce synthetic networks that accurately reflect the structure of both our real-world networks.


Social Network Degree Distribution Middle Region Online Social Network Preferential Attachment 
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.



We are grateful to the Flickr and Yahoo! 360 teams at Yahoo! for their support in data gathering, data analysis, and direction. In particular, we would like to thank Stewart Butterfield, Catarina Fake, Serguei Mourachov, and Neal Sample.


  1. 1.
    L. A. Adamic and E. Adar. How to search a social network. Social Networks, 27(3):187–203, 2005.CrossRefGoogle Scholar
  2. 2.
    R. Albert and A.-L. Barabási. Statistical mechanics of complex networks. Reviews of Modern Physics, 74: 47, 2002.CrossRefGoogle Scholar
  3. 3.
    R. Albert, H. Jeong, and A.-L. Barabasi. Diameter of the world wide web. Nature, 401:130–131, 1999.CrossRefGoogle Scholar
  4. 4.
    A.-L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286:509–512, 1999.PubMedCrossRefGoogle Scholar
  5. 5.
    B. Bollobás. A probabilistic proof of an asymptotic formula for the number of labeled regular graphs. European Journal of Combinatorics, 1:311–316, 1980.Google Scholar
  6. 6.
    B. Bollobas. Random Graphs. Cambridge University Press, Cambridge, UK, 2001.CrossRefGoogle Scholar
  7. 7.
    B. Bollobas and O. Riordan. Mathematical results on scale-free random graphs, In S. Bornholdt and H. G. Schuster, editors, Handbook of Graphs and Networks, pages 1–34. Wiley-VCH, Weinheim, Germany, 2002.Google Scholar
  8. 8.
    A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. L. Wiener. Graph structure in the web. WWW9/Computer Networks, 33(1–6):309–320, 2000.CrossRefGoogle Scholar
  9. 9.
    P. S. Dodds, R. Muhamad, and D. J. Watts. An experimental study of search in global social networks. Science, 301:827–829, 2003.PubMedCrossRefGoogle Scholar
  10. 10.
    S. Dorogovtsev and J. Mendes. Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford University Press, Oxford, England, 2000.Google Scholar
  11. 11.
    S. Dorogovtsev and J. Mendes. Evolution of networks. Advances in Physics, 51:1079–1187, 2002.CrossRefGoogle Scholar
  12. 12.
    P. Erdös and A. Rényi. On random graphs I. Publications Mathematics Debrecen, 6:290–297, 1959.Google Scholar
  13. 13.
    M. Faloutsos, P. Faloutsos, and C. Faloutsos. On power-law relationships of the internet topology. In Proceedings of ACM SIGCOMM Conference, pages 251–262, Cambridge, MA, Aug 1999.Google Scholar
  14. 14.
    D. Fetterly, M. Manasse, M. Najork, and J. Wiener. A large-scale study of the evolution of web pages. Software Practice and Experience, 34(2):213–237, 2004.CrossRefGoogle Scholar
  15. 15.
    J. Kleinberg. The small-world phenomenon: An algorithmic perspective. In Proceedings of the 32nd ACM Symposium on Theory of Computing, pages 163–170, Portland, OR, May 2000.Google Scholar
  16. 16.
    J. Kleinberg. Complex networks and decentralized search algorithms. In Proceedings of the International Congress of Mathematicians, pages 1019–1044, Madrid, Spain, Aug 2006.Google Scholar
  17. 17.
    J. M. Kleinberg. Navigation in a small world. Nature, 406:845, 2000.PubMedCrossRefGoogle Scholar
  18. 18.
    R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. Structure and evolution of blogspace. Communications of the ACM, 47(12):35–39, 2004.CrossRefGoogle Scholar
  19. 19.
    R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. On the bursty evolution of blogspace. World Wide Web Journal, 8(2):159–178, 2005.CrossRefGoogle Scholar
  20. 20.
    R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, and E. Upfal. Stochastic models for the web graph. In Proceedings of 41st Annual Symposium on Foundations of Computer Science, pages 57–65, Redondo Beach, CA, Nov 2000.Google Scholar
  21. 21.
    R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the web for emerging cyber-communities. WWW8/Computer Networks, pages 1481–1493, Toronto, Canada, May 1999.Google Scholar
  22. 22.
    J. Leskovec, L. Backstrom, R. Kumar, and A. Tomkins. Microscopic evolution of social networks. In Proceedings of the 14th ACM International Conference on Knowledge Discovery and Data Mining, pages 462–470, Las Vegas, NV, Aug 2008.Google Scholar
  23. 23.
    J. Leskovec, J. Kleinberg, and C. Faloutsos. Graphs over time: densification laws, shrinking diameters, and possible explanations. In Proceedings of the 11th ACM International Conference on Knowledge Discovery and Data Mining, pages 177–187, Chicago, IL, Aug 2005.Google Scholar
  24. 24.
    J. Leskovec, K. Lang, A. Dasgupta, and M. Mahoney. Statistical properties of community structure in large social and information networks. In Proceedings of the 17th International Conference on World Wide Web, pages 695–704, Beijing, China, Apr 2008.Google Scholar
  25. 25.
    D. Liben-Nowell, J. Novak, R. Kumar, P. Raghavan, and A. Tomkins. Geographic routing in social networks. Proceedings of the National Academy of Sciences, 102(33):11623–11628, 2005.CrossRefGoogle Scholar
  26. 26.
    M. Molloy and B. Reed. A critical point for random graphs with a given degree sequence. Random Structures and Algorithms, 6:161–180, 1995.CrossRefGoogle Scholar
  27. 27.
    M. Newman. The structure and function of complex networks. SIAM Review, 45:167–256, 2003.CrossRefGoogle Scholar
  28. 28.
    M. E. J. Newman, S. H. Strogatz, and D. J. Watts. Random graphs with arbitrary degree distributions and their applications. Physical Review E, 64(2):026118(17 pages), 2001.CrossRefGoogle Scholar
  29. 29.
    A. Ntoulas, J. Cho, and C. Olston. What’s new on the web? the evolution of the web from a search engine perspective. In Proceedings of the 13th International Conference on World Wide Web, pages 1–12, New York, NY, May 2004.Google Scholar
  30. 30.
    S. Strogatz. Exploring complex networks. Nature, 410:268–276, 2001.PubMedCrossRefGoogle Scholar
  31. 31.
    S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge, UK, 1994. Revised, reprinted edition, 1997.Google Scholar
  32. 32.
    D. J. Watts and S. H. Strogatz. Collective dynamics of ‘small-world’ networks. Nature, 393:440–442, 1998.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Yahoo! ResearchSunnyvaleUSA
  2. 2.Google, Inc.Mountain ViewUSA

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