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Identifying important nodes affecting network security in complex networks

Author

Listed:
  • Yongshan Liu
  • Jianjun Wang
  • Haitao He
  • Guoyan Huang
  • Weibo Shi

Abstract

An important node identification algorithm based on an improved structural hole and K-shell decomposition algorithm is proposed to identify important nodes that affect security in complex networks. We consider the global structure of a network and propose a network security evaluation index of important nodes that is free of prior knowledge of network organization based on the degree of nodes and nearest neighborhood information. A node information control ability index is proposed according to the structural hole characteristics of nodes. An algorithm ranks the importance of nodes based on the above two indices and the nodes’ local propagation ability. The influence of nodes on network security and their own propagation ability are analyzed by experiments through the evaluation indices of network efficiency, network maximum connectivity coefficient, and Kendall coefficient. Experimental results show that the proposed algorithm can improve the accuracy of important node identification; this analysis has applications in monitoring network security.

Suggested Citation

  • 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.
  • Handle: RePEc:sae:intdis:v:17:y:2021:i:2:p:1550147721999285
    DOI: 10.1177/1550147721999285
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    References listed on IDEAS

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    1. Dirk Helbing, 2013. "Globally networked risks and how to respond," Nature, Nature, vol. 497(7447), pages 51-59, May.
    2. 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.
    3. Linyuan Lü & Tao Zhou & Qian-Ming Zhang & H. Eugene Stanley, 2016. "The H-index of a network node and its relation to degree and coreness," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
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    Cited by:

    1. Yuan, Yifan & Shen, Xiaohong & Sun, Lin & Yan, Yongsheng & Wang, Haiyan, 2025. "Critical node identification of dynamic-load wireless sensor networks for cascading failure protection," Reliability Engineering and System Safety, Elsevier, vol. 264(PA).

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