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Consistency and differences between centrality measures across distinct classes of networks


  • Stuart Oldham
  • Ben Fulcher
  • Linden Parkes
  • Aurina Arnatkevic̆iūtė
  • Chao Suo
  • Alex Fornito


The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measures have been proposed, but the degree to which they offer unique information, and whether it is advantageous to use multiple centrality measures to define node roles, is unclear. Here we calculate correlations between 17 different centrality measures across 212 diverse real-world networks, examine how these correlations relate to variations in network density and global topology, and investigate whether nodes can be clustered into distinct classes according to their centrality profiles. We find that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations. Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes. Our findings illustrate how network topology shapes the pattern of correlations between centrality measures and demonstrate how a comparative approach to network centrality can inform the interpretation of nodal roles in complex networks.

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  • Stuart Oldham & Ben Fulcher & Linden Parkes & Aurina Arnatkevic̆iūtė & Chao Suo & Alex Fornito, 2019. "Consistency and differences between centrality measures across distinct classes of networks," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-23, July.
  • Handle: RePEc:plo:pone00:0220061
    DOI: 10.1371/journal.pone.0220061

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    References listed on IDEAS

    1. Erjia Yan & Ying Ding, 2009. "Applying centrality measures to impact analysis: A coauthorship network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2107-2118, October.
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    Cited by:

    1. Attila Varga & Norbert Szabó & Tamás Sebestyén, 2020. "Economic impact modelling of smart specialization policy: Which industries should prioritization target?," Papers in Regional Science, Wiley Blackwell, vol. 99(5), pages 1367-1388, October.
    2. Li, Shuying & Zhang, Xian & Xu, Haiyun & Fang, Shu & Garces, Edwin & Daim, Tugrul, 2020. "Measuring strategic technological strength :Patent Portfolio Model," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    3. Gündüç, Semra & Eryiğit, Recep, 2021. "Time dependent correlations between the probability of a node being infected and its centrality measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).

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