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AOGC: An improved gravity centrality based on an adaptive truncation radius and omni-channel paths for identifying key nodes in complex networks

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  • Yang, Pingle
  • Meng, Fanyuan
  • Zhao, Laijun
  • Zhou, Lixin

Abstract

Identifying key nodes that have a significant impact on network structure and function is a fundamental issue with numerous practical applications and technical challenges. The gravity model is a unique method for identifying key nodes that has piqued the interest of many researchers. Most of existing gravity-based methods have limitations in that they neglect the surrounding environment of a node in deeper level such as node location, neighborhood topology, higher-order neighbors, multiple interaction paths, and so on. They assume that inter-node interactions occur only along the shortest path, which is not true and renders them inaccurate in many cases. Based on our defined adaptive truncation radius and “looseness distance” considering omni-channel paths, we propose a new gravity centrality method that integrates multiple node properties and accurately characterizes node interaction distance. Numerical simulations indicate that (i) the gravity model is a good approximation of node interaction effect and (ii) a derivative method outperforms recent high-performing node importance evaluation methods in terms of distinguishing ability, accuracy, spreading ability, and imprecision function with an acceptable time complexity. Furthermore, the proposed method exhibits good stability on networks of varying scales and structural characteristics.

Suggested Citation

  • Yang, Pingle & Meng, Fanyuan & Zhao, Laijun & Zhou, Lixin, 2023. "AOGC: An improved gravity centrality based on an adaptive truncation radius and omni-channel paths for identifying key nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:chsofr:v:166:y:2023:i:c:s0960077922011535
    DOI: 10.1016/j.chaos.2022.112974
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    1. Anastasia Buyalskaya & Marcos Gallo & Colin F. Camerer, 2021. "The golden age of social science," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(5), pages 2002923118-, February.
    2. Dong, Chen & Xu, Guiqiong & Meng, Lei & Yang, Pingle, 2022. "CPR-TOPSIS: A novel algorithm for finding influential nodes in complex networks based on communication probability and relative entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    3. Chen, Jun-Jie & Hu, Mao-Bin & Wu, Yong-Hong, 2022. "Traffic-driven epidemic spreading with non-uniform origin and destination selection," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    4. Cui, Yajuan & Wei, Ruichen & Tian, Yang & Tian, Hui & Zhu, Xuzhen, 2022. "Information propagation influenced by individual fashion-passion trend on multi-layer weighted network," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    5. 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.
    6. 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).
    7. Lv, Changchun & Yuan, Ziwei & Si, Shubin & Duan, Dongli & Yao, Shirui, 2022. "Cascading failure in networks with dynamical behavior against multi-node removal," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    8. Lei, Mingli & Cheong, Kang Hao, 2022. "Node influence ranking in complex networks: A local structure entropy approach," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    9. 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.
    10. Wang, Yan & Li, Haozhan & Zhang, Ling & Zhao, Linlin & Li, Wanlan, 2022. "Identifying influential nodes in social networks: Centripetal centrality and seed exclusion approach," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    11. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    12. Jain, Somya & Sinha, Adwitiya, 2020. "Identification of influential users on Twitter: A novel weighted correlated influence measure for Covid-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    13. Li, Hanwen & Shang, Qiuyan & Deng, Yong, 2021. "A generalized gravity model for influential spreaders identification in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    14. Qu, Junyi & Tang, Ming & Liu, Ying & Guan, Shuguang, 2020. "Identifying influential spreaders in reversible process," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    15. 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.
    16. Wang, Juan & Li, Chao & Xia, Chengyi, 2018. "Improved centrality indicators to characterize the nodal spreading capability in complex networks," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 388-400.
    17. Li, Jie & Wang, Ying & Zhong, Jilong & Sun, Yun & Guo, Zhijun & Chen, Zhiwei & Fu, Chaoqi, 2022. "Network resilience assessment and reinforcement strategy against cascading failure," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    18. Liu, Panfeng & Li, Longjie & Fang, Shiyu & Yao, Yukai, 2021. "Identifying influential nodes in social networks: A voting approach," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    19. 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.
    20. Wen, Tao & Jiang, Wen, 2019. "Identifying influential nodes based on fuzzy local dimension in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 332-342.
    21. Bi, Jialin & Jin, Ji & Qu, Cunquan & Zhan, Xiuxiu & Wang, Guanghui & Yan, Guiying, 2021. "Temporal gravity model for important node identification in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    22. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
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    Cited by:

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