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Comparison of Different Centrality Measures to Find Influential Nodes in Complex Networks

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10658))

Abstract

In this paper, we compare the performance of representative centrality measures, classical and up-to-date, on more real networks in various fields. With the aid of SIR information diffusion model to simulate the vertices’ influence in real networks, we apply the kendall’s tau correlation coefficient, distinguishability and robustness to test different centrality measures at the same level., to show the best application scenarios for certain measure.

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Authors and Affiliations

  1. College of Information Technology and Network Security, People’s Public Security University of China, Beijing, 102600, China

    Fanpeng Meng, Yijun Gu, Shunshun Fu, Mengdi Wang & Yuchen Guo

Authors
  1. Fanpeng Meng
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  2. Yijun Gu
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  3. Shunshun Fu
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  4. Mengdi Wang
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  5. Yuchen Guo
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Corresponding author

Correspondence to Yijun Gu .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Edith Kinney Gaylord Presidential Professor, University of Oklahoma, Norman, Oklahoma, USA

    Mohammed Atiquzzaman

  3. Aalto University, Espoo, Finland

    Zheng Yan

  4. University of Texas at San Antonio, San Antonio, Texas, USA

    Kim-Kwang Raymond Choo

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Meng, F., Gu, Y., Fu, S., Wang, M., Guo, Y. (2017). Comparison of Different Centrality Measures to Find Influential Nodes in Complex Networks. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10658. Springer, Cham. https://doi.org/10.1007/978-3-319-72395-2_38

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  • DOI: https://doi.org/10.1007/978-3-319-72395-2_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72394-5

  • Online ISBN: 978-3-319-72395-2

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