Empirical Analysis of a Dynamic Social Network Built from PGP Keyrings

  • Robert H. Warren
  • Dana Wilkinson
  • Mike Warnecke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4503)


Social networks are the focus of a large body of research. A number of popular email encryption tools make use of online directories to store public key information. These can be used to build a social network of people connected by email relationships. Since these directories contain creation and expiration time-stamps, the corresponding network can be built and analysed dynamically. At any given point, a snapshot of the current state of the model can be observed and traditional metrics evaluated and compared with the state of the model at other times.

We show that, with this described data set, simple traditional predictive measures do vary with time. Moreover, singular events pertinent to the participants in the social network (such as conferences) can be correlated with or implied by significant changes in these measures. This provides evidence that the dynamic behaviour of social networks should not be ignored, either when analysing a real model or when attempting to generate a synthetic model.


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  1. 1.
    Wilson, R.J.: Introduction to graph theory. John Wiley & Sons, Chichester (1986)Google Scholar
  2. 2.
    Gross, J.L., Yellen, J. (eds.): Handbook of graph theory. Discrete mathematics and its applications. CRC press, Boca Raton (2004)zbMATHGoogle Scholar
  3. 3.
    Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge University Press, Cambridge (1994)Google Scholar
  4. 4.
    Carrington, P.J., Scott, J., Wasserman, S. (eds.): Models and methods in social network analysis. Cambridge University Press, Cambridge (2005)Google Scholar
  5. 5.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  6. 6.
    Milgram, S.: The small world problem. Psycology Today (1), 61–67 (1967)Google Scholar
  7. 7.
    Watts, D.: Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton University Press, Princeton (1999)Google Scholar
  8. 8.
    Kleinberg, J.: The Small-World Phenomenon: An Algorithmic Perspective. In: Proceedings of the 32nd ACM Symposium on Theory of Computing (2000)Google Scholar
  9. 9.
    Hannerman, R.A.: Introduction to Social Network Methods. Department of Sociology, University of California (2001)Google Scholar
  10. 10.
    Blaze, M., Feigenbaum, J., Lacy, J.: Decentralized trust management. In: IEEE Symposium on Security and Privacy, pp. 164–173 (1996)Google Scholar
  11. 11.
    Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A.: Graph structure in the web: Experiments and models. In: 9th World Wide Web Conference (2000)Google Scholar
  12. 12.
    Feld, S.L., Elmore, R.: Patterns of sociometric choices: Transitivity reconsidered. Social Psychology Quarterly 45(2), 77–85 (1982)CrossRefGoogle Scholar
  13. 13.
    MacKinnon, I., Warren, R.H.: Age and geographic analysis of the livejournal social network. Technical Report CS-2006-12, School of Computer Science, University of Waterloo (2006)Google Scholar
  14. 14.
    Kumar, R., Novak, J., Raghavan, P., Tomkins, A.: Structure and evolution of blogspace. Commun. ACM 47(12), 35–39 (2004)CrossRefGoogle Scholar
  15. 15.
    Borisov, N., Goldberg, I., Brewer, E.: Off-the-record communication, or, why not to use pgp. In: Workshop on Privacy in the Electronic Society (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Robert H. Warren
    • 1
  • Dana Wilkinson
    • 1
  • Mike Warnecke
    • 2
  1. 1.David R. Cheriton School of Computer Science, University of Waterloo, WaterlooCanada
  2. 2.PSW Applied Research Inc., WaterlooCanada

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