Social Network Analysis on Highly Aggregated Data: What Can We Find?
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.
KeywordsMarketing Coherence Mellon
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- 8.Hanneman, R.A., Riddle, M.: Introduction to Social Network Methods. University of California (2005)Google Scholar
- 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.Kleinfeld, J.: Could It Be A Big World After All? The ”Six Degrees of Separation” Myth. Society (2002)Google Scholar
- 11.Kovanen, L., Saramaki, J., Kaski, K.: Reciprocity of mobile phone calls (2010)Google Scholar
- 17.Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences). Cambridge University Press (1995)Google Scholar