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A novel method of evaluating key nodes in complex networks

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  • Zhu, Canshi
  • Wang, Xiaoyang
  • Zhu, Lin

Abstract

In order to evaluate the influence of nodes in complex networks, a new method is advanced of evaluating key nodes in complex networks, in combination with the “structural hole” theory and closeness centrality of nodes, through defining and applying the influence matrix of nodes’ “structural holes” in response to the limitations of existing methods. The “structural hole” theory gives a comprehensive consideration of the node degree as well as information about topological relations with its neighbors, whereas the closeness centrality of nodes is a reflection of the node's global information. The “structural holes” influence matrix in degree reflects the node's local and global information. So a more proper evaluation standard is established for influence of nodes and a simulation analysis is made of different-scale networks. The results of such analyses show that the method can not only make an exact assessment of the influence of nodes, but also obtain ideal evaluation results from actual complex networks of different scale.

Suggested Citation

  • Zhu, Canshi & Wang, Xiaoyang & Zhu, Lin, 2017. "A novel method of evaluating key nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 96(C), pages 43-50.
    • Handle: RePEc:eee:chsofr:v:96:y:2017:i:c:p:43-50
    DOI: 10.1016/j.chaos.2017.01.007
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Jia, Tao & Qin, Kun & Shan, Jie, 2014. "An exploratory analysis on the evolution of the US airport network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 266-279.
    3. Bae, Joonhyun & Kim, Sangwook, 2014. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 549-559.
    4. Gao, Shuai & Ma, Jun & Chen, Zhumin & Wang, Guanghui & Xing, Changming, 2014. "Ranking the spreading ability of nodes in complex networks based on local structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 130-147.
    5. Tang, Shaoting & Teng, Xian & Pei, Sen & Yan, Shu & Zheng, Zhiming, 2015. "Identification of highly susceptible individuals in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 363-372.
    6. Hu, Ping & Fan, Wenli & Mei, Shengwei, 2015. "Identifying node importance in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 169-176.
    7. Linyuan Lü & Yi-Cheng Zhang & Chi Ho Yeung & Tao Zhou, 2011. "Leaders in Social Networks, the Delicious Case," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-9, June.
    8. Li, Qian & Zhou, Tao & Lü, Linyuan & Chen, Duanbing, 2014. "Identifying influential spreaders by weighted LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 47-55.
    9. Lai, Darong & Lu, Hongtao & Nardini, Christine, 2010. "Finding communities in directed networks by PageRank random walk induced network embedding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2443-2454.
    10. Kandiah, Vivek & Shepelyansky, Dima L., 2012. "PageRank model of opinion formation on social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5779-5793.
    11. Erjia Yan & Ying Ding, 2009. "Applying centrality measures to impact analysis: A coauthorship network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2107-2118, October.
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