Centrality Robustness and Link Prediction in Complex Social Networks

  • Søren Atmakuri Davidsen
  • Daniel Ortiz-Arroyo


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.


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.


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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

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