θg -模糊图与传递性：优化互联网络中的数据传输

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems Pub Date : 2025-08-05 DOI:10.1016/j.fss.2025.109549

Aswathi Prabhath , Sunil Mathew , J.N. Mordeson

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引用次数: 0

摘要

为了确保网络内的有效通信，最佳的数据流对互连网络至关重要。因此，重要的是要考虑模糊图结构表示网络，以尽量减少流量的中断和随之而来的网络故障。本文讨论了具有上述性质的一类特殊模糊图——θg -模糊图。除了对θg -模糊图的刻画外，本文利用结构内一对顶点之间的广义循环连通性讨论了一种新的传递性，即θg -传递性。得到了模糊块和线形图等模糊图结构的广义循环连通性。提出了θg -模糊图在网络理论中的一个应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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θg - fuzzy graphs and transitivity: Optimizing data transmission in interconnection networks

An optimal flow of data is essential for interconnection networks in order to ensure effective communication within the network. Hence, it is important to consider fuzzy graph structures representing networks which minimizes disruption of the flow and consequent failure of the network. This article discusses

θ^{g}

- fuzzy graphs, a special category of fuzzy graphs having the properties mentioned above. Apart from a characterization for

θ^{g}

- fuzzy graphs, this paper discusses a new kind of transitivity, called

θ^{g}

- transitivity, using the generalized cycle connectivity between a pair of vertices within the structure. Generalized cycle connectivity of certain fuzzy graph structures like fuzzy blocks and line graphs is also obtained. An application of

θ^{g}

- fuzzy graphs in network theory is also proposed in the paper.

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来源期刊

Fuzzy Sets and Systems 数学-计算机：理论方法

CiteScore

6.50

自引率

17.90%

发文量

321

审稿时长

6.1 months

期刊介绍： Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies. In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.