Social Network Analysis on Highly Aggregated Data: What Can We Find?

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 186)

Abstract

Social network analysis techniques have been often used to derive useful knowledge from email and communication networks. However, most previous works considered an ideal scenario when full raw data were available for analysis. Unfortunately, such data raise privacy issues, and are often considered too valuable to be disclosed. In this paper we present the results of social network analysis of a very large volume of the telecommunication data acquired from a mobile phone operator. The data are highly aggregated, with only limited amount of information about individual connections between users. We show that even with such limited data, social network analysis methods provide valuable insights into the data and can reveal interesting patterns.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adamic, L.A.: The Small World Web. In: Abiteboul, S., Vercoustre, A.-M. (eds.) ECDL 1999. LNCS, vol. 1696, pp. 443–452. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Aggarwal, C.C., Yu, P.S.: A general survey of privacy-preserving data mining models and algorithms. In: Privacy-Preserving Data Mining. The Kluwer International Series on Advances in Database Systems, vol. 34, ch. 2, pp. 11–52. Springer US, Boston (2008)CrossRefGoogle Scholar
  3. 3.
    Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: Proc. of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006, pp. 44–54. ACM, New York (2006)CrossRefGoogle Scholar
  4. 4.
    Barabási, A.-L., Albert, R.: Emergence of Scaling in Random Networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Bonneau, J., Anderson, J., Danezis, G.: Prying Data out of a Social Network. In: Social Network Analysis and Mining, International Conference on Advances in, pp. 249–254. IEEE, Los Alamitos (2009)CrossRefGoogle Scholar
  6. 6.
    Dodds, P.S., Muhamad, R., Watts, D.J.: An Experimental Study of Search in Global Social Networks. Science 301(5634), 827–829 (2003)CrossRefGoogle Scholar
  7. 7.
    Gross, R., Acquisti, A.: Information revelation and privacy in online social networks. In: Proc. of the 2005 ACM Workshop on Privacy in the Electronic Society, WPES 2005, pp. 71–80. ACM, New York (2005)CrossRefGoogle Scholar
  8. 8.
    Hanneman, R.A., Riddle, M.: Introduction to Social Network Methods. University of California (2005)Google Scholar
  9. 9.
    Kleinberg, J.: The Small-World Phenomenon: An Algorithmic Perspective. In: Proc. of the 32nd ACM Symposium on Theory of Computing, pp. 163–170 (2000)Google Scholar
  10. 10.
    Kleinfeld, J.: Could It Be A Big World After All? The ”Six Degrees of Separation” Myth. Society (2002)Google Scholar
  11. 11.
    Kovanen, L., Saramaki, J., Kaski, K.: Reciprocity of mobile phone calls (2010)Google Scholar
  12. 12.
    Krishnamurthy, B., Wills, C.E.: Characterizing privacy in online social networks. In: Proc. of the First Workshop on Online Social Networks, WOSN 2008, pp. 37–42. ACM, New York (2008)CrossRefGoogle Scholar
  13. 13.
    Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Proc. of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006, pp. 611–617. ACM, New York (2006)CrossRefGoogle Scholar
  14. 14.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proc. of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD 2005, pp. 177–187. ACM, New York (2005)CrossRefGoogle Scholar
  15. 15.
    Onnela, J.P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., Barabási, A.L.: Structure and tie strengths in mobile communication networks. Proc. of the National Academy of Sciences 104(18), 7332–7336 (2007)CrossRefGoogle Scholar
  16. 16.
    Travers, J., Milgram, S.: An Experimental Study of the Small World Problem. Sociometry 32(4), 425–443 (1969)CrossRefGoogle Scholar
  17. 17.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences). Cambridge University Press (1995)Google Scholar
  18. 18.
    Zheleva, E., Getoor, L.: Preserving the Privacy of Sensitive Relationships in Graph Data. In: Bonchi, F., Malin, B., Saygın, Y. (eds.) PInKDD 2007. LNCS, vol. 4890, pp. 153–171. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznanPoland

Personalised recommendations