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Computational Social Networks pp 197–224Cite as

Centrality Robustness and Link Prediction in Complex Social Networks

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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.

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Notes

  1. 1.

    It should be noted that this function is dependent on the size of network that has to be generated.

  2. 2.

    Practical memory limit for holding adjacency matrices in our computing system is less than 25,000 nodes.

  3. 3.

    This approach was used in [16].

  4. 4.

    t(e) is the time-stamp when edge e was created, and t(u) the time-stamp when u joined the network.

  5. 5.

    This function was found using standard linear least squares method, and the pearson correlation as fitness function between power fit and exponential fit.

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Authors and Affiliations

  1. Computational Intelligence and Security Laboratory, Department of Electronic Systems, Aalborg University, Esbjerg, Denmark

    Søren Atmakuri Davidsen & Daniel Ortiz-Arroyo

Authors
  1. Søren Atmakuri Davidsen
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  2. Daniel Ortiz-Arroyo
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Correspondence to Søren Atmakuri Davidsen .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs SNIRE, Auburn, Washington, USA

    Ajith Abraham

  2. Faculty of Computers and Information, Information Technology Department, Cairo University, Ahmed Zewal Street 5, Orman, Giza, 12613, Egypt

    Aboul-Ella Hassanien

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© 2012 Springer-Verlag London

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Davidsen, S.A., Ortiz-Arroyo, D. (2012). Centrality Robustness and Link Prediction in Complex Social Networks. In: Abraham, A., Hassanien, AE. (eds) Computational Social Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4048-1_8

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  • DOI: https://doi.org/10.1007/978-1-4471-4048-1_8

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  • Print ISBN: 978-1-4471-4047-4

  • Online ISBN: 978-1-4471-4048-1

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