A Study of the Spatio-Temporal Correlations in Mobile Calls Networks

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 615)

Abstract

For the last few years, the amount of data has significantly increased in the companies. It is the reason why data analysis methods have to evolve to meet new demands. In this article, we introduce a practical analysis of a large database from a telecommunication operator. The problem is to segment a territory and characterize the retrieved areas owing to their inhabitant behavior in terms of mobile telephony. We have call detail records collected during five months in France. We propose a two stages analysis. The first one aims at grouping source antennas which originating calls are similarly distributed on target antennas and conversely for target antenna w.r.t. source antenna. A geographic projection of the data is used to display the results on a map of France. The second stage discretizes the time into periods between which we note changes in distributions of calls emerging from the clusters of source antennas. This enables an analysis of temporal changes of inhabitants behavior in every area of the country.

References

  1. Airoldi, E., D.M. Blei, S.E. Fienberg, and E.P. Xing. 2008. Mixed membership stochastic blockmodels. JMLR 9: 1981–2014.MATHGoogle Scholar
  2. Blondel, V.D., J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics 2008(10): P10008\(+\).Google Scholar
  3. Blondel, V.D., G. Krings, and I. Thomas. 2010. Regions and borders of mobile telephony in belgium and in the brussels metropolitan zone. The e-journal for Academic Research on Brussels 42: 1–12.Google Scholar
  4. Boullé, M. 2011a. Data grid models for preparation and modeling in supervised learning. In Hands-On Pattern Recognition: Challenges in Machine Learning vol. 1, 99–130. Microtome.Google Scholar
  5. Boullé, M. 2011b. Estimation de la densité d’arcs dans les graphes de grande taille: une alternative à la détection de clusters. In EGC, 353–364.Google Scholar
  6. Boullé, M. 2012. Functional data clustering via piecewise constant nonparametric density estimation. Pattern Recognition 45(12): 4389–4401.MATHCrossRefGoogle Scholar
  7. Cover, T.M., and J.A. Thomas. 2006. Elements of information theory (2. ed.). Wiley.Google Scholar
  8. Doreian, P., V. Batagelj, and A. Ferligoj. 2004. Generalized blockmodeling of two-mode network data. Social Networks 26(1): 29–53.CrossRefGoogle Scholar
  9. Grünwald, P. 2007. The minimum description length principle. MIT Press.Google Scholar
  10. Guigourès, R., and M. Boullé. 2011. Segmentation of towns using call detail records. NetMob Workshop at IEEE SocialCom.Google Scholar
  11. Guigourès, R., M. Boullé, and F. Rossi. 2012. A triclustering approach for time evolving graphs. In IEEE 12th International Conference on Data Mining Workshops (ICDMW), 115–122.Google Scholar
  12. Jaynes, E. 2003. Probability theory: The logic of science. Cambridge University Press.Google Scholar
  13. Kemp, C., and J. Tenenbaum. 2006. Learning systems of concepts with an infinite relational model. In 21st National Conference on Artificial Intelligence.Google Scholar
  14. Nadel, S.F. 1957. The theory of social structure. London: Cohen & West.Google Scholar
  15. Nadif, M., and G. Govaert. 2010. Model-based co-clustering for continuous data. In ICMLA, 175–180.Google Scholar
  16. Newman, M. 2006. Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23): 8577–8582.CrossRefGoogle Scholar
  17. Nowicki, K., and T. Snijders. 2001. Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association 96: 1077–1087.MATHMathSciNetCrossRefGoogle Scholar
  18. Reichardt, J., and D.R. White. 2007. Role models for complex networks. The European Physical Journal B 60: 217–224.MATHCrossRefGoogle Scholar
  19. Shannon, C.E. 1948. A mathematical theory of communication. Bell System Technical Journal 27: 379–423.MATHMathSciNetCrossRefGoogle Scholar
  20. Strehl, A., and J. Ghosh. 2003. Cluster ensembles—a knowledge reuse framework for combining multiple partition. JMLR 3: 583–617.MATHMathSciNetGoogle Scholar
  21. Wasserman, S., and K. Faust. 1994. Social Network Analysis: Methods and Applications. Structural analysis in the social sciences. Cambridge University Press.Google Scholar
  22. White, H., S. Boorman, and R. Breiger. 1976. Social structure from multiple networks: I. blockmodels of roles and positions. American Journal of Sociology 81(4): 730–780.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Romain Guigourès
    • 1
    • 2
  • Marc Boullé
    • 1
  • Fabrice Rossi
    • 2
  1. 1.Orange LabsLannionFrance
  2. 2.SAMM EA 4543 - Université Paris 1ParisFrance

Personalised recommendations