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Robustness of Centrality Measures Under Incomplete Data

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Complex Networks & Their Applications XII (COMPLEX NETWORKS 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1143))

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Abstract

Understanding of real systems relies on the identification of its central elements. Over the years, a large number of centrality measures have been proposed to assess the importance of nodes in complex networks. However, most real networks are incomplete and contain incorrect data, resulting in a high sensitivity of centrality indices. In this paper, we examine the robustness of centrality to the presence of errors in the network structure. Our experiments are performed on weighted and unweighted real-world networks ranging from the criminal network to the trade food network. As a result, we discuss a sensitivity of centrality measures to different data imputation techniques.

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Notes

  1. 1.

    In the case of node addition, we assume that the added node was initially isolated.

  2. 2.

    The dominating set of centrality \(x\) includes a list of centrality measures, which are more sensitive than \(x\) to the graph modification based on the pairwise comparison. Similarly, the dominated set of \(x\) contains centralities that are less sensitive than \(x\).

  3. 3.

    We have also performed the analysis for \(k=10\%\) and the overall results are highly agreed with \(k=5\%\), even though the centrality measures are less stable.

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Acknowledgment

The article was prepared within the framework of the HSE University Basic Research Program and funded by the Russian Academic Excellence Project ‘5–100’. The analysis of centrality measures (Sects. 2–3) was supported by grant No. MK-3867.2022.1.6.

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Authors and Affiliations

  1. HSE University, Moscow, Russia

    Natalia Meshcheryakova & Sergey Shvydun

Authors
  1. Natalia Meshcheryakova
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  2. Sergey Shvydun
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Corresponding author

Correspondence to Natalia Meshcheryakova .

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Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  3. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  4. Department of Economics, Yildiz Technical University, Istanbul, Türkiye

    Murat Donduran

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Meshcheryakova, N., Shvydun, S. (2024). Robustness of Centrality Measures Under Incomplete Data. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-031-53472-0_27

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  • DOI: https://doi.org/10.1007/978-3-031-53472-0_27

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  • Print ISBN: 978-3-031-53471-3

  • Online ISBN: 978-3-031-53472-0

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