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Using mapping entropy to identify node centrality in complex networks

Author

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  • Nie, Tingyuan
  • Guo, Zheng
  • Zhao, Kun
  • Lu, Zhe-Ming

Abstract

The problem of finding the best strategy to attack a network or immunize a population with a minimal number of nodes has attracted much current research interest. The assessment of node importance has been a fundamental issue in the research of complex networks. In this paper, we propose a new concept called mapping entropy (ME) to identify the importance of a node in the complex network. The concept is established according to the local information which considers the correlation among all neighbors of a node. We evaluate the efficiency of the centrality by static and dynamic attacks on standard network models and real-world networks. The simulation result shows that the new centrality is more efficient than traditional attack strategies, whether it is static or dynamic.

Suggested Citation

  • Nie, Tingyuan & Guo, Zheng & Zhao, Kun & Lu, Zhe-Ming, 2016. "Using mapping entropy to identify node centrality in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 290-297.
    • Handle: RePEc:eee:phsmap:v:453:y:2016:i:c:p:290-297
    DOI: 10.1016/j.physa.2016.02.009
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    Citations

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    Cited by:

    1. Ni, Chengzhang & Yang, Jun & Kong, Demei, 2020. "Sequential seeding strategy for social influence diffusion with improved entropy-based centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Yu, Hui & Cao, Xi & Liu, Zun & Li, Yongjun, 2017. "Identifying key nodes based on improved structural holes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 318-327.
    3. Yang, Yu & He, Ze & Song, Zouying & Fu, Xin & Wang, Jianwei, 2018. "Investigation on structural and spatial characteristics of taxi trip trajectory network in Xi’an, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 755-766.
    4. Saxena, Chandni & Doja, M.N. & Ahmad, Tanvir, 2020. "Entropy based flow transfer for influence dissemination in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    5. Liang He & Shouwei Li, 2017. "Network Entropy and Systemic Risk in Dynamic Banking Systems," Complexity, Hindawi, vol. 2017, pages 1-7, November.
    6. Tuğal, İhsan & Karcı, Ali, 2019. "Comparisons of Karcı and Shannon entropies and their effects on centrality of social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 352-363.
    7. Wang, Min & Li, Wanchun & Guo, Yuning & Peng, Xiaoyan & Li, Yingxiang, 2020. "Identifying influential spreaders in complex networks based on improved k-shell method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    8. Jing, Weiwei & Xu, Xiangdong & Pu, Yichao, 2020. "Route redundancy-based approach to identify the critical stations in metro networks: A mean-excess probability measure," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

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