Abstract

A parameter is a numerical factor whose values help us to identify a system. Connectivity parameters are essential in the analysis of connectivity of various kinds of networks. In graphs, the strength of a cycle is always one. But, in a fuzzy incidence graph (FIG), the strengths of cycles may vary even for a given pair of vertices. Cyclic reachability is an attribute that decides the overall connectedness of any network. In graph the cycle connectivity (CC) from vertex a to vertex b and from vertex b to vertex a is always one. In fuzzy graph (FG) the CC from vertex a to vertex b and from vertex b to vertex a is always same. But if someone is interested in finding CC from vertex a to an edge ab, then graphs and FGs cannot answer this question. Therefore, in this research article, we proposed the idea of CC for FIG. Because in FIG, we can find CC from vertex a to vertex b and also from vertex a to an edge ab. Also, we proposed the idea of CC of fuzzy incidence cycles (FICs) and complete fuzzy incidence graphs (CFIGs). The fuzzy incidence cyclic cut-vertex, fuzzy incidence cyclic bridge, and fuzzy incidence cyclic cut pair are established. A condition for CFIG to have fuzzy incidence cyclic cut-vertex is examined. Cyclic connectivity index and average cyclic connectivity index of FIG are also investigated. Three different types of vertices, such as cyclic connectivity increasing vertex, cyclically neutral vertex and, cyclic connectivity decreasing vertex, are also defined. The real-life applications of CC of FIG in a highway system of different cities to minimize road accidents and a computer network to find the best computers among all other computers are also provided.

Highlights

  • Graphs are convenient tools to explain associations between different types of entities under examination

  • The membership value (MSV) of the vertices is indicating data store in each of these computers, the MSV of the edges is demonstrating the total amount of data that can be transferred from one computer to another computer and the MSV of the incidence pair (Ip) is representing the amount of data which one computer is transferring to another computer

  • We advanced the theory of fuzzy incidence graph (FIG)

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Summary

Introduction

Graphs are convenient tools to explain associations between different types of entities under examination. Three kinds of operations, including direct product, semi-strong product, and Cyclic connectivity index of fuzzy incidence graphs with applications strong product for interval-valued FGs was provided by Rashmanlou, and Jun [27]. Like node and edge connectivity in graphs, Mathew et al [40] discussed these concepts for FIGs. Mordeson and Mathew [41] developed fuzzy end nodes and fuzzy incidence cut vertices in FIGs. Nazeer et al [42] presented the idea of intuitionistic fuzzy incidence graphs (IFIGs) as a generalization of FIGs along with their certain properties.

Preliminaries
Cycle connectivity of fuzzy incidence graphs
Average cyclic connectivity index of fuzzy incidence graph
Real-life applications of cycle connectivity
Application of cycle connectivity in highway system
Application of cycle connectivity in a computer network
Comparative analysis
Conclusion
Full Text
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