Advertisement

Centrality Robustness and Link Prediction in Complex Social Networks

  • Søren Atmakuri Davidsen
  • Daniel Ortiz-Arroyo
Chapter

Abstract

This chapter addresses two important issues in social network analysis that involve uncertainty. Firstly, we present an analysis on the robustness of centrality measures that extends the work presented in Borgatti et al. using three types of complex network structures and one real social network. Secondly, we present a method to predict edges in dynamic social networks. Our experimental results indicate that the robustness of the centrality measures applied to more realistic social networks follows a predictable pattern and that the use of temporal statistics could improve the accuracy achieved on edge prediction.

Keywords

Centrality Measure Target Node Link Prediction True Network Preferential Attachment Model 
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.

References

  1. 1.
    Albert, R., Barabási, A.L.: Emergence of scaling in random networks. Science 286, 509–512 (1999)MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Albert, R., Barabási, A.L., Jeong, H.: Mean-field theory for scale-free random networks. Physica A 272, 173–187 (1999). doi:10. 1016/S0378-4371(99)00291-5CrossRefGoogle Scholar
  3. 3.
    Barabási, A.L., Bonabeau, E.: Scale-free networks. Sci. Am. 288(5), 50–59 (2003)CrossRefGoogle Scholar
  4. 4.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: structure and dynamics. Phys. Rep. 424, 175–308 (2006). doi:10.1016/j.physrep.2005.10.009MathSciNetCrossRefGoogle Scholar
  5. 5.
    Borgatti, S.P., Carley, K.M., Krackhardt, D.: On the robustness of centrality measures under conditions of imperfect data. Soc. Netw. 28(2), 124–136 (2005). doi:10.1016/j.socnet.2005.05.001CrossRefGoogle Scholar
  6. 6.
    Clauset, A., Moore, C., Newman, M.E.J.: Hierarchical structure and the prediction of missing links in networks. Nature 453, 98–101 (2008). doi:10.1038/nature06830CrossRefGoogle Scholar
  7. 7.
    Geng, X., Wang, Y.: Degree correlations in citation networks model with aging. Europhys. Lett. 88, 38002 (2009). doi:10.1209/0295-5075/ 88/38002CrossRefGoogle Scholar
  8. 8.
    Gloor, P.A., Niepel, S., Li, Y.: Identifying potential suspects by temporal link analysis. Technical Reports, MIT CCS (2006)Google Scholar
  9. 9.
    Gloor, P.A., Zhao, Y.: Tecflow – a temporal communication flow visualizer for social network analysis. In: ACM CSCW Workshop on Social Networks, ACM CSCW Conference (2005)Google Scholar
  10. 10.
    Goldenberg, A., Zheng, A.X., Fienberg, S.E., Airoldi, E.M.: A survey of statistical network models. Found. Trends Mach. Learn. 2(2), 1–117 (2009)Google Scholar
  11. 11.
    Kashima, H., Abe, N.: A parameterized probabilistic model of network evolution for supervised link prediction. In: Proceedings of the 6th International Conference on Data Mining, pp. 340–349. IEEE Computer Society (2006). doi:10.1109/ICDM.2006.8Google Scholar
  12. 12.
    Klemm, K., Eguiluz, V.M.: Highly clustered scale-free networks. Phys. Rev. E 65(3), 036123 (2002). doi:10.1103/PhysRevE.65.03612CrossRefGoogle Scholar
  13. 13.
    Krebs, V.E.: Uncloaking terrorist networks. First Monday 7(4) (2002)Google Scholar
  14. 14.
    Krebs, V., Holley, J.: Building smart communities through network weavingGoogle Scholar
  15. 15.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery in data mining, pp. 177–187 (2005). doi:10.1145/ 1081870.1081893Google Scholar
  16. 16.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007). doi:10.1002/asi.v58:7CrossRefGoogle Scholar
  17. 17.
    Milgram, S.: The small world problem. Psychol. Today 2, 60–67 (1967)Google Scholar
  18. 18.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Ortiz-Arroyo, D., Hussain, D.M.A.: An information theory approach to identify sets of key players. In: Intelligence and Security Informatics, vol. 5376/2008, pp. 15–26. Springer (2008). doi:10.1007/ 978-3-540-89900-6{ _}5Google Scholar
  20. 20.
    Scott, J.: Social Network Analysis: A Handbook, 2nd edn. SAGE, London (2000)Google Scholar
  21. 21.
    Strogatz, S.H.: Exploring complex networks. Nature 410, 268–276 (2001). doi:10.1038/35065725CrossRefGoogle Scholar
  22. 22.
    Tang, J., Musolesi, M., Mascolo, C., Latora, V.: Temporal distance metrics for social network analysis. In: WOSN ’09: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 31–36. ACM, New York (2009). doi:10.1145/1592665.1592674Google Scholar
  23. 23.
    Watts, D.J., Strogatz, S.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998). doi:10.1038/30918CrossRefGoogle Scholar
  24. 24.
    Xiang, E.W.: A survey on link prediction models for social network data. Technical Reports, The Hong Kong University of Science and Technology (2008)Google Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Søren Atmakuri Davidsen
    • 1
  • Daniel Ortiz-Arroyo
    • 1
  1. 1.Computational Intelligence and Security Laboratory, Department of Electronic SystemsAalborg UniversityEsbjergDenmark

Personalised recommendations