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Finding important nodes via improved cycle ratio method

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  • Huang, Yihao
  • Peng, Weijun
  • Zheng, Muhua
  • Zhao, Ming
  • Zhao, Manrui
  • Zhang, Yicheng

Abstract

The cycle ratio method is designed to define the importance of nodes by the cycles of a network, and a set of important nodes identified by this method has superior control performance than by degree centrality, H-index, and coreness methods in several aspects such as spreading, percolation, and pinning control. Unfortunately, the method is not precise enough to portray the importance of the nodes, so in this paper, we improve the cycle ratio method by reducing the impact of four and larger cycles and adding the effects of the tree structure. Through numerical simulations on several real networks, we find that the set of important nodes discovered by the improved cycle ratio method is more dispersed and has better control in all three aspects of spreading, percolation, and pinning control than the original cycle ratio method. The work in this paper makes it more accurate to use the cycle structure to find a set of important nodes in a network and provides new ideas for a deeper understanding of the effects of local structure on the importance of the nodes.

Suggested Citation

  • Huang, Yihao & Peng, Weijun & Zheng, Muhua & Zhao, Ming & Zhao, Manrui & Zhang, Yicheng, 2025. "Finding important nodes via improved cycle ratio method," Chaos, Solitons & Fractals, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:chsofr:v:190:y:2025:i:c:s0960077924012980
    DOI: 10.1016/j.chaos.2024.115746
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    References listed on IDEAS

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    1. H. Jeong & S. P. Mason & A.-L. Barabási & Z. N. Oltvai, 2001. "Lethality and centrality in protein networks," Nature, Nature, vol. 411(6833), pages 41-42, May.
    2. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    3. 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.
    4. 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.
    5. Korn, A. & Schubert, A. & Telcs, A., 2009. "Lobby index in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(11), pages 2221-2226.
    6. Wang, Xiao Fan & Chen, Guanrong, 2002. "Pinning control of scale-free dynamical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 310(3), pages 521-531.
    7. Tianlong Fan & Hao Li & Xiao-Long Ren & Shuqi Xu & Youzhao Gou & Linyuan Lü, 2021. "The rise and fall of countries on world trade web: A network perspective," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 32(08), pages 1-19, August.
    Full references (including those not matched with items on IDEAS)

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