中国物理B ›› 2025, Vol. 34 ›› Issue (10): 108904-108904.doi: 10.1088/1674-1056/adfefc

所属专题： SPECIAL TOPIC — Computational programs in complex systems

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Identification of vital nodes based on global and local features in hypergraphs

Li Liang(梁丽), Li-Yao Qi(齐丽瑶), and Shi-Cai Gong(龚世才)†   

  1. School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China
  • 收稿日期:2025-06-24 修回日期:2025-08-04 接受日期:2025-08-26 发布日期:2025-10-20
  • 通讯作者: Shi-Cai Gong E-mail:gongsc@zust.edu.cn

Identification of vital nodes based on global and local features in hypergraphs

Li Liang(梁丽), Li-Yao Qi(齐丽瑶), and Shi-Cai Gong(龚世才)†   

  1. School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China
  • Received:2025-06-24 Revised:2025-08-04 Accepted:2025-08-26 Published:2025-10-20
  • Contact: Shi-Cai Gong E-mail:gongsc@zust.edu.cn

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摘要： Hypergraphs, which encapsulate interactions of higher-order beyond mere pairwise connections, are essential for representing polyadic relationships within complex systems. Consequently, an increasing number of researchers are focusing on the centrality problem in hypergraphs. Specifically, researchers are tackling the challenge of utilizing higher-order structures to effectively define centrality metrics. This paper presents a novel approach, LGK, derived from the K-shell decomposition method, which incorporates both global and local perspectives. Empirical evaluations indicate that the LGK method provides several advantages, including reduced time complexity and improved accuracy in identifying critical nodes in hypergraphs.

关键词: hypergraph, vital nodes, K-shell decomposition, susceptible-infected-recovered (SIR) model

Abstract: Hypergraphs, which encapsulate interactions of higher-order beyond mere pairwise connections, are essential for representing polyadic relationships within complex systems. Consequently, an increasing number of researchers are focusing on the centrality problem in hypergraphs. Specifically, researchers are tackling the challenge of utilizing higher-order structures to effectively define centrality metrics. This paper presents a novel approach, LGK, derived from the K-shell decomposition method, which incorporates both global and local perspectives. Empirical evaluations indicate that the LGK method provides several advantages, including reduced time complexity and improved accuracy in identifying critical nodes in hypergraphs.

Key words: hypergraph, vital nodes, K-shell decomposition, susceptible-infected-recovered (SIR) model

中图分类号:  (Complex systems)

  • 89.75.-k
89.75.Fb (Structures and organization in complex systems) 05.45.-a (Nonlinear dynamics and chaos)