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Identifying Critical Nodes of Collaboration Networks Based on Improved K-shell Decomposition |
Zhang Dayong1( ),Men Hao2,Su Zhan1 |
1Key Laboratory of Interactive Media Design and Equipment Service Innovation, Harbin Institute of Technology, Harbin 150001, China 2Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China |
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Abstract [Objective] This paper proposes an improved K-shell decomposition algorithm based on semi-local centrality, aiming to address the degradation issue of critical nodes identification. [Methods] First, we constructed a semi-local centrality index based on the nodes’ first-order neighbor information. Then, we determined the final key node set by recursive removal, with the semi-local information of the remaining and removed nodes. [Results] We examined our algorithm with six groups of cooperative networks. It could effectively eliminate the degradation issue of the original algorithm with high computational accuracy and low computational complexity. [Limitations] Due to the influence of network structures, the calculation accuracy of some sample networks was lower than that of the betweenness centrality algorithm. [Conclusions] The new algorithm can improve the stability of the collaboration network and identify key node sets in large-scale practical networks.
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Received: 22 May 2023
Published: 15 March 2024
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Fund:National Social Science Fund of China(21BDJ062);Emerging Interdisciplinary Innovation Program of Harbin Institute of Technology(SYL-JC-202203) |
Corresponding Authors:
Zhang Dayong, ORCID:0000-0001-9122-2220, E-mail: yongzhhit@163.com。
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