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A modified efficiency centrality to identify influential nodes in weighted networks

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Abstract

It is still a crucial issue to identify influential nodes effectively in the study of complex networks. As for the existing efficiency centrality (EffC), it cannot be applied to a weighted network. In this paper, a modified efficiency centrality (EffC\(^\mathrm{m}\)) is proposed by extending EffC into weighted networks. The proposed measure trades off the node degree and global structure in a weighted network. The influence of both the sum of the average degree of nodes in the whole network and the average distance of the network is taken into account. Numerical examples are used to illustrate the efficiency of the proposed method.

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Acknowledgements

The authors greatly appreciate the reviewers’ suggestions and the editor’s encouragement. The work was partially supported by the National Natural Science Foundation of China (Grant Nos 61573290, 61503237).

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Authors and Affiliations

  1. School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Yunchuan Wang

  2. School of Computer Science, Sichuan University, Chengdu, China

    Shasha Wang

  3. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China

    Yong Deng

  4. Big Data Decision Institute, Jinan University, Guangzhou, China

    Yong Deng

  5. School of Computer and Information Science, Southwest University, Chongqing, China

    Yong Deng

Authors
  1. Yunchuan Wang
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  2. Shasha Wang
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  3. Yong Deng
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Correspondence to Yong Deng.

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Wang, Y., Wang, S. & Deng, Y. A modified efficiency centrality to identify influential nodes in weighted networks. Pramana - J Phys 92, 68 (2019). https://doi.org/10.1007/s12043-019-1727-1

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  • DOI: https://doi.org/10.1007/s12043-019-1727-1

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