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模糊强割点和模糊强割边的一种刻画

A characterization for fuzzy strong cut vertices and fuzzy strong cut edges.

作者信息

Ma Junye, Shen Lijing, Li Lin

机构信息

School of Applied Science, Taiyuan University of Science and Technology, Taiyuan, 030024, People's Republic of China.

School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, People's Republic of China.

出版信息

Sci Rep. 2024 Jul 4;14(1):15403. doi: 10.1038/s41598-024-66274-9.

DOI:10.1038/s41598-024-66274-9
PMID:38965327
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11224279/
Abstract

Cut vertices and cut edges are valuable for analyzing connectivity problems in classical graph theory. However, they cannot deal with certain fuzzy problems. In order to solve this problem, this paper introduces the definitions of fuzzy strong cut vertices and fuzzy strong cut edges, and characterizes fuzzy strong cut vertices and fuzzy strong cut edges in fuzzy trees, complete fuzzy graphs, and fuzzy cycles. Finally, practical applications verify the effectiveness of the theory in network stability analysis.

摘要

割点和割边对于分析经典图论中的连通性问题很有价值。然而,它们无法处理某些模糊问题。为了解决这个问题,本文引入了模糊强割点和模糊强割边的定义,并刻画了模糊树、完全模糊图和模糊圈中的模糊强割点和模糊强割边。最后,实际应用验证了该理论在网络稳定性分析中的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11224279/b931a39ea089/41598_2024_66274_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11224279/eba8fed01f66/41598_2024_66274_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11224279/8fd24d4f2ed6/41598_2024_66274_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11224279/e70b1210dd8f/41598_2024_66274_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11224279/b931a39ea089/41598_2024_66274_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11224279/eba8fed01f66/41598_2024_66274_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11224279/8fd24d4f2ed6/41598_2024_66274_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11224279/e70b1210dd8f/41598_2024_66274_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9aef/11224279/b931a39ea089/41598_2024_66274_Figa_HTML.jpg

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本文引用的文献

1
Estimation of most effected cycles and busiest network route based on complexity function of graph in fuzzy environment.基于模糊环境下的图复杂度函数估计受影响最大的周期和最繁忙的网络路线。
Artif Intell Rev. 2022;55(6):4557-4574. doi: 10.1007/s10462-021-10111-2. Epub 2022 Jan 10.
2
Clustering algorithm with strength of connectedness for m-polar fuzzy network models.基于连通强度的 m-极化模糊网络模型聚类算法。
Math Biosci Eng. 2022 Jan;19(1):420-455. doi: 10.3934/mbe.2022021. Epub 2021 Nov 16.