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Identifying influential nodes in complex networks from global perspective

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  • Zhao, Jie
  • Wang, Yunchuan
  • Deng, Yong

Abstract

How to identify influential nodes in complex networks is an open issue. Several centrality measures have been proposed to address this. But these studies concentrate only on only one aspect. To solve this problem, a novel method to identify influential nodes is proposed, which takes into account not only the importance of itself but also the influence of all nodes in the graph into consideration. This approach has superiority in identifying nodes that seem unimportant but are important in the complex network. Besides, it provides a quantitative model to measure the global importance of each node (GIN). The comparison experiments conducted on six different networks illustrate the effectiveness of the proposed method.

Suggested Citation

  • Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:chsofr:v:133:y:2020:i:c:s0960077920300369
    DOI: 10.1016/j.chaos.2020.109637
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    References listed on IDEAS

    as
    1. Berahmand, Kamal & Bouyer, Asgarali & Samadi, Negin, 2018. "A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 41-54.
    2. Xuelong Li & Marko Jusup & Zhen Wang & Huijia Li & Lei Shi & Boris Podobnik & H. Eugene Stanley & Shlomo Havlin & Stefano Boccaletti, 2018. "Punishment diminishes the benefits of network reciprocity in social dilemma experiments," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(1), pages 30-35, January.
    3. Du, Zhouyang & Tang, Jinjun & Qi, Yong & Wang, Yiwei & Han, Chunyang & Yang, Yifan, 2020. "Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    4. Li, Zhibin & Jia, Danyang & Guo, Hao & Geng, Yini & Shen, Chen & Wang, Zhen & Li, Xuelong, 2019. "The effect of multigame on cooperation in spatial network," Applied Mathematics and Computation, Elsevier, vol. 351(C), pages 162-167.
    5. Bian, Tian & Hu, Jiantao & Deng, Yong, 2017. "Identifying influential nodes in complex networks based on AHP," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 422-436.
    6. Dai, Zhen & Li, Ping & Chen, Yan & Zhang, Kai & Zhang, Jie, 2019. "Influential node ranking via randomized spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    7. Zhu, Peican & Wang, Xing & Zhi, Qiang & Ma, Jiezhong & Guo, Yangming, 2018. "Analysis of epidemic spreading process in multi-communities," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 231-237.
    8. Wu, Jiehua & Shen, Jing & Zhou, Bei & Zhang, Xiayan & Huang, Bohuai, 2019. "General link prediction with influential node identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 996-1007.
    9. Mo, Hongming & Deng, Yong, 2019. "Identifying node importance based on evidence theory in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 529(C).
    10. Liu, Jian-Guo & Ren, Zhuo-Ming & Guo, Qiang, 2013. "Ranking the spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4154-4159.
    11. Guo, Tian & Guo, Mi & Zhang, Yan & Liang, Shuanglu, 2019. "The effect of aspiration on the evolution of cooperation in spatial multigame," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 27-32.
    12. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2018. "A novel visibility graph transformation of time series into weighted networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 201-208.
    13. 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.
    14. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    15. Wen, Xiangxi & Tu, Congliang & Wu, Minggong & Jiang, Xurui, 2018. "Fast ranking nodes importance in complex networks based on LS-SVM method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 11-23.
    16. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    17. Ye, Ye & Hang, Xiao Rong & Koh, Jin Ming & Miszczak, Jarosław Adam & Cheong, Kang Hao & Xie, Neng-gang, 2020. "Passive network evolution promotes group welfare in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    18. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    19. Li, Meizhu & Zhang, Qi & Deng, Yong, 2018. "Evidential identification of influential nodes in network of networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 283-296.
    20. Lv, Zhiwei & Zhao, Nan & Xiong, Fei & Chen, Nan, 2019. "A novel measure of identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 488-497.
    21. Eric E. Schadt, 2009. "Molecular networks as sensors and drivers of common human diseases," Nature, Nature, vol. 461(7261), pages 218-223, September.
    22. Wen, Tao & Deng, Yong, 2020. "The vulnerability of communities in complex networks: An entropy approach," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    23. 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.
    24. Jiang, Lincheng & Zhao, Xiang & Ge, Bin & Xiao, Weidong & Ruan, Yirun, 2019. "An efficient algorithm for mining a set of influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 58-65.
    25. Yang, Hanchao & liu, Yujia & Wan, Qian & Deng, Yong, 2019. "A bio-inspired optimal network division method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    26. Zhu, Peican & Wang, Xinyu & Li, Shudong & Guo, Yangming & Wang, Zhen, 2019. "Investigation of epidemic spreading process on multiplex networks by incorporating fatal properties," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 512-524.
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