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Temporal gravity model for important node identification in temporal networks

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  • Bi, Jialin
  • Jin, Ji
  • Qu, Cunquan
  • Zhan, Xiuxiu
  • Wang, Guanghui
  • Yan, Guiying

Abstract

Identifying important nodes in networks is essential to analysing their structure and understanding their dynamical processes. In addition, myriad real systems are time-varying and can be represented as temporal networks. Motivated by classic gravity in physics, we propose a temporal gravity model to identify important nodes in temporal networks. In gravity, the attraction between two objects depends on their masses and distance. For the temporal network, we treat basic node properties (e.g., static and temporal properties) as the mass and temporal characteristics (i.e., fastest arrival distance and temporal shortest distance) as the distance. Experimental results on 10 real datasets show that the temporal gravity model outperforms baseline methods in quantifying the structural influence of nodes. When using the temporal shortest distance as the distance between two nodes, the proposed model is more robust and more accurately determines the node spreading influence than baseline methods. Furthermore, when using the temporal information to quantify the mass of each node, we found that a novel robust metric can be used to accurately determine the node influence regarding both network structure and information spreading.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:147:y:2021:i:c:s0960077921002885
    DOI: 10.1016/j.chaos.2021.110934
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    Cited by:

    1. Hao, Yucheng & Jia, Limin & Zio, Enrico & Wang, Yanhui & He, Zhichao, 2023. "A multi-objective optimization model for identifying groups of critical elements in a high-speed train," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Tao, Li & Kong, Shengzhou & He, Langzhou & Zhang, Fan & Li, Xianghua & Jia, Tao & Han, Zhen, 2022. "A sequential-path tree-based centrality for identifying influential spreaders in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    3. 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).

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