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EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks

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  • Zareie, Ahmad
  • Sheikhahmadi, Amir

Abstract

In recent research, the importance of determining social network users’ spreading influence and ranking them has attracted plenty of attention. H-index is one of the methods that have been presented for this purpose, and determines the spreading capability of a node based on the degrees of its neighbors. In this method, part of the information on the neighbors is disregarded, which reduces ranking accuracy. In this paper, a measure is presented for specification of the centrality of nodes through extension of the H-index notion. The results of experimentation over real-world and artificial datasets demonstrate that the proposed measure exhibits higher accuracy and efficiency than in the other compared methods.

Suggested Citation

  • Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:141-155
    DOI: 10.1016/j.physa.2018.09.064
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    References listed on IDEAS

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    Cited by:

    1. Deming Lin & Tianhui Gong & Wenbin Liu & Martin Meyer, 2020. "An entropy-based measure for the evolution of h index research," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2283-2298, December.
    2. Yongshan Liu & Jianjun Wang & Haitao He & Guoyan Huang & Weibo Shi, 2021. "Identifying important nodes affecting network security in complex networks," International Journal of Distributed Sensor Networks, , vol. 17(2), pages 15501477219, February.

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