| SPECIAL TOPIC — Computational programs in complex systems |
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Vital nodes identification method integrating degree centrality and cycle ratio |
| Yu Zhao(赵玉)1,2 and Bo Yang(杨波)1,2,† |
1 Data Science Research Center, Kunming University of Science and Technology, Kunming 650500, China;
2 Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China |
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Abstract Identifying vital nodes is one of the core issues of network science, and is crucial for epidemic prevention and control, network security maintenance, and biomedical research and development. In this paper, a new vital nodes identification method, named degree and cycle ratio (DC), is proposed by integrating degree centrality (weight $\alpha$) and cycle ratio (weight $1-\alpha$). The results show that the dynamic observations and weight $\alpha$ are nonlinear and non-monotonicity (i.e., there exists an optimal value $\alpha^*$ for $\alpha$), and that DC performs better than a single index in most networks. According to the value of $\alpha ^{\ast } $, networks are classified into degree-dominant networks ($\alpha ^{\ast }>0.5 $) and cycle-dominant networks ($\alpha ^{\ast }<0.5 $). Specifically, in most degree-dominant networks (such as Chengdu-BUS, Chongqing-BUS and Beijing-BUS), degree is dominant in the identification of vital nodes, but the identification effect can be improved by adding cycle structure information to the nodes. In most cycle-dominant networks (such as Email, Wiki and Hamsterster), the cycle ratio is dominant in the identification of vital nodes, but the effect can be notably enhanced by additional node degree information. Finally, interestingly, in Lancichinetti-Fortunato-Radicchi (LFR) synthesis networks, the cycle-dominant network is observed.
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Received: 09 October 2024
Revised: 07 December 2024
Accepted manuscript online:
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PACS:
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89.75.Fb
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(Structures and organization in complex systems)
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64.60.aq
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(Networks)
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| Fund: Project supported by Yunnan Fundamental Research Projects (Grant No. 202401AT070359). |
Corresponding Authors:
Bo Yang
E-mail: yangbo@kust.edu.cn
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Cite this article:
Yu Zhao(赵玉) and Bo Yang(杨波) Vital nodes identification method integrating degree centrality and cycle ratio 2025 Chin. Phys. B 34 038901
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