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A novel measure of identifying influential nodes in complex networks

Author

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  • Lv, Zhiwei
  • Zhao, Nan
  • Xiong, Fei
  • Chen, Nan

Abstract

Research about ranking nodes according to their spreading ability in complex networks is a fundamental and essential issue. As one of the vital centrality measures, the degree centrality is very simple. However, it is difficult to distinguish the importance of nodes with the same degree. Global metrics such as betweenness centrality and closeness centrality can identify influential nodes more accurately, but there remains some disadvantages and limitations. In this paper, we propose an average shortest path centrality to rank the spreaders, in which the relative change of the average shortest path of the whole network is taken into account. For evaluating the performance, we adapt Susceptible–Infected–Recovered model to simulate the epidemic spreading process on four different real networks. The experimental and simulated results show that our scheme owns better performance compared with degree, betweenness and closeness centrality.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:523:y:2019:i:c:p:488-497
    DOI: 10.1016/j.physa.2019.01.136
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    References listed on IDEAS

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    5. Chaharborj, Sarkhosh Seddighi & Nabi, Khondoker Nazmoon & Feng, Koo Lee & Chaharborj, Shahriar Seddighi & Phang, Pei See, 2022. "Controlling COVID-19 transmission with isolation of influential nodes," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
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    7. Sheng, Jinfang & Dai, Jinying & Wang, Bin & Duan, Guihua & Long, Jun & Zhang, Junkai & Guan, Kerong & Hu, Sheng & Chen, Long & Guan, Wanghao, 2020. "Identifying influential nodes in complex networks based on global and local structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    8. Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Exploring node importance evolution of weighted complex networks in urban rail transit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
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    10. Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).

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