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A centrality measure for communication ability in weighted network

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  • Zhai, Li
  • Yan, Xiangbin
  • Zhang, Guojing

Abstract

This paper proposes a new node centrality measurement in a weighted network, the communication centrality, which is inspired by Hirsch’s h-index. We investigated the properties of the communication centrality, and proved that the distribution of the communication centrality has the power-law upper tail in weighted scale-free networks. Relevant measures for node and network are discussed as extensions. A case study of a scientific collaboration network indicates that the communication centrality is different from other common centrality measures and other h-type indexes. Communication centrality displays moderate correlation with other indexes, and contains a well-balanced mix of other centrality measures and cannot be replaced by any of them.

Suggested Citation

  • Zhai, Li & Yan, Xiangbin & Zhang, Guojing, 2013. "A centrality measure for communication ability in weighted network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6107-6117.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:23:p:6107-6117
    DOI: 10.1016/j.physa.2013.07.056
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    References listed on IDEAS

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    6. András Schubert, 2012. "A Hirsch-type index of co-author partnership ability," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 303-308, April.
    7. Alonso, S. & Cabrerizo, F.J. & Herrera-Viedma, E. & Herrera, F., 2009. "h-Index: A review focused in its variants, computation and standardization for different scientific fields," Journal of Informetrics, Elsevier, vol. 3(4), pages 273-289.
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    Cited by:

    1. Li Zhai & Xiujuan Li & Xiangbin Yan & Weiguo Fan, 2014. "Evolutionary analysis of collaboration networks in the field of information systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1657-1677, December.
    2. Zhai, Li & Yan, Xiangbin & Zhang, Guojing, 2018. "Bi-directional h-index: A new measure of node centrality in weighted and directed networks," Journal of Informetrics, Elsevier, vol. 12(1), pages 299-314.

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