# Analysis of the Shortest Path in Spherical Fuzzy Networks Using the Novel Dijkstra Algorithm

Authors: Zafar Ullah; Huma Bashir; Rukhshanda Anjum; Salman A. AlQahtani; Suheer Al-Hadhrami; Abdul Ghaffar
Journal: Mathematical Problems in Engineering
 (2021)

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## Abstract

The concept of fuzzy graph (FG) and its generalized forms has been developed to cope with several real-life problems having some sort of imprecision like networking problems, decision making, shortest path problems, and so on. This paper is based on some developments in generalization of FG theory to deal with situation where imprecision is characterized by four types of membership grades. A novel concept of T-spherical fuzzy graph (TSFG) is proposed as a common generalization of FG, intuitionistic fuzzy graph (IFG), and picture fuzzy graph (PFG) based on the recently introduced concept of T-spherical fuzzy set (TSFS). The significance and novelty of proposed concept is elaborated with the help of some examples, graphical analysis, and results. Some graph theoretic terms are defined and their properties are studied. Specially, the famous Dijkstra algorithm is proposed in the environment of TSFGs and is applied to solve a shortest path problem. The comparative analysis of the proposed concept and existing theory is made. In addition, the advantages of the proposed work are discussed over the existing tools.


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## Body

## 1. Introduction

In the past decades, the development of graph theory, specifically the fuzzy graph (FG) theory, and its applications in numerous scientific subjects indicates its significance. The addition of FGs in graph theory is of worth as it increases the viability of graph theory. From application point of view, FGs have been widely utilized in practical problems, for example, reference [1] provided a list of possible regions handled by FGs and fuzzy hypergraphs, reference [2] modelled some traffic problems using FGs, reference [3] utilized FGs in optimization of networks, reference [4] is based on application of telecommunication system in FGs, and reference [5] applied FGs in fuzzy neural networks. The theory of FGs has been initiated in [6] but briefly elaborated in [7] by Rosenfield after the remarkable work of Zadeh [8] on fuzzy sets (FSs). For some works on FGs, one may refer to [9–16].An FS only described the membership grade of an event/object while the non-membership grade is obtained by subtracting the membership grade from 1, i.e., the non-membership grade could not be chosen independently. Therefore, Atanassov [17] developed the theory of intuitionistic fuzzy set (IFS) as an advanced form of FSs and provided an opening for the theory of IFGs which was proposed in [18]. Atanassov’s tool of IFSs gave strength to Zadeh’s FSs, and in the same way, theory of IFG generalizes FGs and makes it more valuable. For some quality work on IFGs and its applications, one may refer to [19–23]. IFSs could not model human opinion properly as described in [24, 25], and hence a new tool of picture fuzzy set (PFS) was introduced describing not only yes or no type situations but also situations having some abstinence or refusal grade involved like in voting situation. PFS strongly generalizes FSs and IFSs, and some useful work in this direction could be found in [26–30]. The idea of PFGs was developed in [31] generalizing the FGs and IFGs.If we observe the structure of PFSs, it is clear that they generalize the FSs and IFSs. They know how to handle the situations or data that FSs or IFSs might not. But the structure of PFS has some limitations. Its constraint on the membership, abstinence, and non-membership grades states that their sum must be less than or equal to one. Due to this formation of PFSs, one is unable to assign the values to these membership, abstinence, and non-membership functions by their own choice. Keeping this issue in mind, Mahmood et al. [32] proposed the concept of spherical fuzzy sets (SFSs) and consequently T-spherical fuzzy sets (TSFSs), which improves the construction of PFS and does not have limitations at all. Such type of framework of TSFSs not only models human opinion other than yes or no but also can deal with any form of data without any limitations. For example, if we look at the constraint of PFSs and TSFSs, then it becomes very much clear that the framework of TSFSs has no limitations. The constraints of IFSs, PFSs, and TSFSs are as follows:(i)
For IFSsA=xi,sxi,dxi, we have 0≤sxi+dxi≤1.(ii)
For PFSsA=xi,sxi,ixi,dxi, we have 0≤sxi+ixi+dxi≤1.(iii)
For TSFSsA=xi,sxi,ixi,dxi, we have 0≤snxi+inxi+dnxi≤1 for some n∈ℤ+.The diverse structure and novelty of TSFSs is clear from its constraints and comparison with existing structures. Further diversity of proposed structure is discussed in Section2 with the aid of some pictorial representations in Figures 1–5 .Figure 1
Intuitionistic fuzzy space.Figure 2
Pictorial fuzzy space.Figure 3
Space of spherical fuzzy sets [32].Figure 4
TSFS forn = 5.Figure 5
TSFS forn = 10.The problem of the shortest path is one of the well-known problems that has been discussed prominently in various extended structures of FSs. Okada and Soper [33] worked out the shortest path problems utilizing fuzzy arcs, and Deng et al. [34] presented the Dijkstra algorithm that is the technique for finding out the shortest path. References [35–37] provide some good work on fuzzy shortest path problems. Gani and Jabarulla [38] also studied the shortest paths in the environment of IFSs, and for details on finding out the shortest path in an IFG using Dijkstra algorithm, see [39]. Plenty of works have been carried out on the topic of shortest path problems (one may refer to [40–43]).As discussed, the framework of TSFS is more generalized than FS, IFS, and PFS. Therefore, the graph of TSFS could be more useful in dealing with uncertain situations. Keeping in view the developments in FG, IFG, and PFG and their several real-life applications, the aim of this study is to propose the graphs of TSFSs named as TSFGs. The TSFG generalizes the FG, IFG, and PFG. It discusses the membership, abstinence, and non-membership grades of an entity. Moreover, there is complete freedom for a decision maker; they can assign any fuzzy number as the membership, abstinence, or non-membership grades. Unlike PFGs, there are no limitations in the structure of TSFGs. The TSFG can handle any problem that its predecessors could handle. It is the most powerful modelling tool among all the existing tools. The new structure of TSFG is investigated and some related terms are defined. The terms subgraph, complement, degree, and strength are defined for TSFGs and supported with examples. Several operations are also defined for TSFG, and some examples are discussed to support the defined concepts. To discuss the diversity and significance of TSFGs, a shortest path problem in the environment of TSFSs and TSFGs is also studied.This paper is organized as follows. Section1 is based on some history and motivation for proposing TSFGs. In Section 2, the basic definitions of IFS, PFS, SFS, TSFS, IFG, PFG, and the novelty of the proposed idea are discussed with the help of geometrical shapes. Also, in this section, TSFGs are proposed along with some basic graph theoretic terms like complement of TSFGs, order, degree and size of TSFGs and subgraphs of TSFGs, and the results of proposed notions are studied. In Section 3, operations of join and union are defined for TSFGs along with Cartesian product and composition of TSFGs. In Section 4, we propose a modified Dijkstra algorithm for the TSF shortest path that is then applied to find out the shortest path in a network. Furthermore, a comparative study is provided. In Section 5, we discussed the summary of our work along with its advantages and some future directions.


## 2. Preliminaries

In this section, the basic definitions of IFSs, PFSs, and TSFSs are reviewed and their spaces are geometrically described. Some elementary definitions of graphs of IFS and PFS are also discussed and explained with the help of some examples.Definition 1 (see [17]).
LetX be a universal set. An IFS on X is characterized by two mappings Ŧ and F on 0,1 given that 0≤sx+dx≤1. The values of s and d in the unit interval described the grade of membership and grade of non-membership of an element x in X. Also, 1−sx+dx denotes the hesitancy of x∈X. Moreover, the duplet s,d is said to be an intuitionistic fuzzy number (IFN). The range of the IFNs is portrayed in Figure 1.
In the voting situations, we might end up with four types of statuses, i.e., vote against, vote in favour, refusal, and abstain (nor in favour nor against). IFSs cannot cope with issues like this. Realizing this, a novel concept of PFS was developed by B. C. Cuong in 2013.Definition 2 (see [25]).
LetX be a universal set. A PFS on X is characterized by three mappings s, i, and d on 0,1 provided that 0≤sx+ix+dx≤1. The value of s, i, and d in the interval [0, 1] describes the membership, abstinence, and non-membership grades of x inX. Also, 1−sx+ix+dx denoted the refusal grade of x∈X. The triplet s,i,d is called the picture fuzzy number (PFN). The space of PFNs is depicted in Figure 2.
The problem with the framework of PFS is its check on the grade mappings, as depicted in Figure2. Realizing this concern, Tahir et al. [32] proposed SFSs and consequently TSFSs. The definition of TSFSs is described below. Moreover, in order to make the point clear that TSFSs generalize IFSs and PFSs, a pictorial representation is given.Definition 3 (see [32]).
An SFS onX (a universal set) consists of three mappings s, i, and d on 0,1 provided that 0≤s2x+i2x+d2x≤1. The values of s, i, and d in the interval [0, 1] describe the membership, abstinence, and non-membership grades of x in X. Also, the refusal grade of x∈X is denoted by rx=1−s2x+i2x+d2x. The triplet s,i,d is called a spherical fuzzy number (SFN).Definition 4 (see [32]).
A TSFS onX (a universal set) consists of three mappings s, i, and d on 0,1 provided that 0≤snx+inx+dnx≤1 for some n∈ℤ. The values of s, i, and d in the interval [0, 1] describe the grade of membership, grade of abstinence, and grade of non-membership of x in X. Also, the refusal grade of x∈X is denoted by rx=1−snx+inx+dnxn. The triplet s,i,d is called a T-spherical fuzzy number (TSFN).
The following figures described SFSs and TSFSs geometrically presenting their innovation and diverse structure. Figures3–5 also show that TSFSs have no limitation.
From all the observations in this section, we conclude that the concept of TSFSs is the generalization of FSs, IFSs, PFSs, and SFSs and their structure does not have any limitations. Now, some elementary definitions associated with the graphs of IFS and PFS are discussed providing a base for the proposed work.Definition 5 (see [25]).
A pairƓ=N,Ę is known as IFG if(i)
N=x1,x2,x3,x4,…,xn is the collection of vertices such that s1:N⟶0,1 and d1:N⟶0,1 denote the grade of membership and grade of non-membership of the element xi∈N, respectively, with the condition that 0≤s1+d1≤1 for all xi∈N,i∈I.(ii)
Ě⊆N×N where s2:N×N⟶0,1 and d2:N×N⟶0,1 denote the grade of membership and grade of non-membership of the element xi,xj∈Ě such that s2xi,xj≤mins1xi,s1xj and d2xi,xj≤maxd1xi,d1xj with the condition 0≤s2xi,xj+d2xi,xj≤1 for all xi,xj∈Ę,i∈I.Example 1.
Figure6 is an example of IFG.Figure 6
Intuitionistic fuzzy graph.Definition 6 (see [31]).
A pairƓ=N,Ę is said to be a PFG if(i)
N=x1,x2,x3,x4,…,xn is the set of vertices such that s1:N⟶0,1 describes the grade of membership, i1:N⟶0,1 describes the grade of abstinence, and d1:N⟶0,1 describes the grade of non-membership of the element xi∈N on the condition that 0≤s1xi+i1xi+d1xi≤1 for all xi∈N,i∈I, and 1−s1i+i1i+d1i is known as refusal grade of x in N.(ii)
Ě⊆N×N where s2:N×N⟶0,1 describes the grade of membership, i:N×N⟶0,1 describes the grade of abstinence, and d2:N×N⟶0,1 describes the grade of non-membership of the element xi,xj∈Ě such that s2xi,xj≤mins1xi,s1xj, i2xi,xj≤mini1xi,i1xj and d2xi,xj≤maxd1xi,d1xj with the condition 0≤s2xi,xj+i2xi,xj+d2xi,xj≤1 for all xi,xj∈Ę,i∈I, and 1−s2xi,xj+i2xi,xj+d2xi,xj is known as refusal grade of xi,xj in Ę.Example 2.
LetƓ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę. Figure 7 is an example of PFG.Figure 7
Picture fuzzy graph.


### 2.1. T-Spherical Fuzzy Graphs

Definition 7 (see [43]).
A pairƓ=N,Ę is said to be TSFG if(i)
N=x1,x2,x3,…,xn is the set of vertices such that s1:N⟶0,1 describes the grade of membership, i1:N⟶0,1 describes the grade of abstinence, and d1:N⟶0,1 describes the grade of non-membership of the element xi∈N on the condition that for some positive integers n0≤s1nxi+i1nxi+d1nxi≤1 for all xi∈Ni∈I, and 1−s1nxi+i1nxi+d1nxin is known as refusal grade of x in N.(ii)
Ę⊆N×N where s2:N×N⟶0,1,i2:N×N⟶0,1 and d2:N×N⟶0,1 describes the grades of membership, abstinence, and non-membership of the element xi,xj∈Ę such that s2xi,xj≤mins1xi,s1xj, i2xi,xj≤mini1xi,i1xj and d2xi,xj≤maxd1xi,d1xj with the condition 0≤s2nxi,xj+i2nxi,xj+d2nxi,xj≤1 for all xi,xj∈Ę, and 1−s2nxi,xj+i2nxi,xj+d2nxi,xjn is known as refusal grade of xi,xj in Ę.Example 3.
LetGˇ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę.
The vertices shown in Figures8 and 9 are purely T-spherical fuzzy numbers (TSFNs) for n=5.Figure 8
T-spherical fuzzy graph.Figure 9
An example of not T-spherical fuzzy graph.Remark 1.
PFG and SFG are TSFGs, but generally, the converse is not true.Example 4.
The graph in Figure8 is clearly TSFG, but it is neither PFG nor SFG. Consider 0.8,0.9,0.8; then, 0.8+0.9+0.8=2.5≰1 and 0.82+0.92+0.82=2.09≰1.Definition 8 (see [43]).
For TSFNsA=sA,ıA,dA and B=sB,ıB,dB, we define(1)A⊕B=t˙,sAnx+sBnx−sAnx⋅sBnxn,ıAnx+ıBnx−ıAnx⋅ıBnxniidA⋅dBi,A⊗B=ϰ,sAx⋅sBx,iıAx⋅ıBx,idAnx+dBnx−dAnx⋅dBnxni.
In FS theory, the rules of comparison have always been a challenge. For IFSs, several score functions have been established regularly. These score functions fall under the title of comparison rules. A better score function (SF) for IFSs is established in [39] and it discusses the limitations of existing score functions which is demonstrated using examples. Further, work done on PFSs is significantly less; hence, in literature, there does not exist any SFs. Therefore, this article establishes a novel SF as a generalized SF proposed in [39]. In Section 4, this SF shall be utilized in the problems of the shortest path.Definition 9 (see [43]).
The SF for a TSFNA=s,i,d is defined as(2)SCA=sn1−in−dn3,SCA∈0,1.Remark 2.
Replacingi=0 and n=1 reduces the defined score function in the environment of IFSs.Definition 10 (see [43]).
A pairH=N∗,Ę∗ is said to be T-spherical fuzzy subgraph (TSFSG) of TSFG Ɠ=N,Ę if N∗⊆N and Ę∗⊆Ę, that is, s1i∗≤s1i, i1i∗≤i1i, d1i∗≥d1i and s2ij∗≤s2ij, i2ij∗≤i2ij, d2ij∗≥d2ij for all i,j=1,2,…,n.Definition 11 (see [43]).
The complement of a TSFGƓ=N,Ę is defined as(i)
N¯=N.(ii)
si¯=si, ii¯=ii and di¯=di for i=1,2,…,n.(iii)
s2ij¯=minsi,isj−s2ij, i2ij¯=minii,iij−s2ij and d2ij¯=maxdi,idj−d2ij for any i,j=1,2,…,n.Example 5.
Figures10 and 11 are examples of complement of TSFG.
The vertices are purely TSFNs forn=3 in Figures 10 and 11.Figure 10
T-spherical fuzzy graph forn = 3.Figure 11
Complement of Figure10.Definition 12 (see [43]).
The degree of a TSFGƓ=N,Ę is denoted and is defined by d˜x=d˜sx,d˜ix,d˜dx, where d˜sx=∑y≠xs2x,yi, d˜ix=∑y≠xi2x,yi and d˜dx=∑y≠xd˜2x,yi for ix,y∈N.Example 6.
LetƓ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę.
The vertices are purely TSFNs forn=4 in Figure 12.
The degree of vertices shown in Figure12 is(3)d˜x1=0.9,1.1,1.3,d˜x2=1,1,1.1,d˜x3=0.8,0.8,1.4,d˜x4=0.7,0.9,1.6.Figure 12
For the degree of TSFG.Definition 13 (see [43]).
A pairƓ=N,Ę is said to be strong TSFG if(i)
N=x1,x2,x3,…,xn is the set of vertices such that s:N⟶0,1 denotes the grade of membership, i:N⟶0,1 denotes the grade of abstinence, and d:N⟶0,1 represents the grade of non-membership of the element xi∈N with the condition that for some positive integers n0≤s1nxi+i1nxi+d1nxi≤1 for all xi∈Ni∈I, and 1−s1nxi+i1nxi+d1nxin is known as refusal grade of x in N.(ii)
Ę⊆N×N where s2:N×N⟶0,1 denotes the grade of membership, i2:N×N⟶0,1 describes the grade of abstinence, and d2:N×N⟶0,1 represents the grade of non-membership of the element xi,xj∈Ę such that s2xi,xj=mins1xi,s1xj, i2xi,xj=mini1xi,i1xj and d2xi,xj=maxd1xi,d1xj with the condition 0≤s2nxi,xj+i2nxi,xj+d2nxi,xj≤1 for all xi,xj∈Ę, and 1−s2nxi,xj+i2nxi,xj+d2nxi,xjn is known as refusal grade of xi,ixj in Ę.Example 7.
Figure13 is an example of strong TSFG.
The vertices are purely TSFNs forn=4 in Figure 13.Figure 13
Strong T-spherical fuzzy graph.Definition 14 (see [43]).
An edgexi,xj in a TSFG Ɠ=N,Ę is known to be a bridge, if by removal of that edge decreases the strength of the connectedness among any pair of vertices in Ɠ.Example 8.
LetƓ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę.
Here,x1,x4 is a bridge.
The vertices shown in Figure14 are purely TSFNs for n=5.Figure 14
TSFG for a bridge.Definition 15 (see [43]).
A vertexxi in a TSFG Ɠ=N,Ę is known to be cut vertex, if the removal of that vertex decreases the strength of the connectedness among any pair of vertices.Example 9.
LetƓ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę.
Here,x1 is a cut vertex.
The vertices are purely TSFNs forn=3 in Figure 15.Figure 15
T-spherical fuzzy graph for cut vertex.


## 2.1. T-Spherical Fuzzy Graphs

Definition 7 (see [43]).
A pairƓ=N,Ę is said to be TSFG if(i)
N=x1,x2,x3,…,xn is the set of vertices such that s1:N⟶0,1 describes the grade of membership, i1:N⟶0,1 describes the grade of abstinence, and d1:N⟶0,1 describes the grade of non-membership of the element xi∈N on the condition that for some positive integers n0≤s1nxi+i1nxi+d1nxi≤1 for all xi∈Ni∈I, and 1−s1nxi+i1nxi+d1nxin is known as refusal grade of x in N.(ii)
Ę⊆N×N where s2:N×N⟶0,1,i2:N×N⟶0,1 and d2:N×N⟶0,1 describes the grades of membership, abstinence, and non-membership of the element xi,xj∈Ę such that s2xi,xj≤mins1xi,s1xj, i2xi,xj≤mini1xi,i1xj and d2xi,xj≤maxd1xi,d1xj with the condition 0≤s2nxi,xj+i2nxi,xj+d2nxi,xj≤1 for all xi,xj∈Ę, and 1−s2nxi,xj+i2nxi,xj+d2nxi,xjn is known as refusal grade of xi,xj in Ę.Example 3.
LetGˇ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę.
The vertices shown in Figures8 and 9 are purely T-spherical fuzzy numbers (TSFNs) for n=5.Figure 8
T-spherical fuzzy graph.Figure 9
An example of not T-spherical fuzzy graph.Remark 1.
PFG and SFG are TSFGs, but generally, the converse is not true.Example 4.
The graph in Figure8 is clearly TSFG, but it is neither PFG nor SFG. Consider 0.8,0.9,0.8; then, 0.8+0.9+0.8=2.5≰1 and 0.82+0.92+0.82=2.09≰1.Definition 8 (see [43]).
For TSFNsA=sA,ıA,dA and B=sB,ıB,dB, we define(1)A⊕B=t˙,sAnx+sBnx−sAnx⋅sBnxn,ıAnx+ıBnx−ıAnx⋅ıBnxniidA⋅dBi,A⊗B=ϰ,sAx⋅sBx,iıAx⋅ıBx,idAnx+dBnx−dAnx⋅dBnxni.
In FS theory, the rules of comparison have always been a challenge. For IFSs, several score functions have been established regularly. These score functions fall under the title of comparison rules. A better score function (SF) for IFSs is established in [39] and it discusses the limitations of existing score functions which is demonstrated using examples. Further, work done on PFSs is significantly less; hence, in literature, there does not exist any SFs. Therefore, this article establishes a novel SF as a generalized SF proposed in [39]. In Section 4, this SF shall be utilized in the problems of the shortest path.Definition 9 (see [43]).
The SF for a TSFNA=s,i,d is defined as(2)SCA=sn1−in−dn3,SCA∈0,1.Remark 2.
Replacingi=0 and n=1 reduces the defined score function in the environment of IFSs.Definition 10 (see [43]).
A pairH=N∗,Ę∗ is said to be T-spherical fuzzy subgraph (TSFSG) of TSFG Ɠ=N,Ę if N∗⊆N and Ę∗⊆Ę, that is, s1i∗≤s1i, i1i∗≤i1i, d1i∗≥d1i and s2ij∗≤s2ij, i2ij∗≤i2ij, d2ij∗≥d2ij for all i,j=1,2,…,n.Definition 11 (see [43]).
The complement of a TSFGƓ=N,Ę is defined as(i)
N¯=N.(ii)
si¯=si, ii¯=ii and di¯=di for i=1,2,…,n.(iii)
s2ij¯=minsi,isj−s2ij, i2ij¯=minii,iij−s2ij and d2ij¯=maxdi,idj−d2ij for any i,j=1,2,…,n.Example 5.
Figures10 and 11 are examples of complement of TSFG.
The vertices are purely TSFNs forn=3 in Figures 10 and 11.Figure 10
T-spherical fuzzy graph forn = 3.Figure 11
Complement of Figure10.Definition 12 (see [43]).
The degree of a TSFGƓ=N,Ę is denoted and is defined by d˜x=d˜sx,d˜ix,d˜dx, where d˜sx=∑y≠xs2x,yi, d˜ix=∑y≠xi2x,yi and d˜dx=∑y≠xd˜2x,yi for ix,y∈N.Example 6.
LetƓ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę.
The vertices are purely TSFNs forn=4 in Figure 12.
The degree of vertices shown in Figure12 is(3)d˜x1=0.9,1.1,1.3,d˜x2=1,1,1.1,d˜x3=0.8,0.8,1.4,d˜x4=0.7,0.9,1.6.Figure 12
For the degree of TSFG.Definition 13 (see [43]).
A pairƓ=N,Ę is said to be strong TSFG if(i)
N=x1,x2,x3,…,xn is the set of vertices such that s:N⟶0,1 denotes the grade of membership, i:N⟶0,1 denotes the grade of abstinence, and d:N⟶0,1 represents the grade of non-membership of the element xi∈N with the condition that for some positive integers n0≤s1nxi+i1nxi+d1nxi≤1 for all xi∈Ni∈I, and 1−s1nxi+i1nxi+d1nxin is known as refusal grade of x in N.(ii)
Ę⊆N×N where s2:N×N⟶0,1 denotes the grade of membership, i2:N×N⟶0,1 describes the grade of abstinence, and d2:N×N⟶0,1 represents the grade of non-membership of the element xi,xj∈Ę such that s2xi,xj=mins1xi,s1xj, i2xi,xj=mini1xi,i1xj and d2xi,xj=maxd1xi,d1xj with the condition 0≤s2nxi,xj+i2nxi,xj+d2nxi,xj≤1 for all xi,xj∈Ę, and 1−s2nxi,xj+i2nxi,xj+d2nxi,xjn is known as refusal grade of xi,ixj in Ę.Example 7.
Figure13 is an example of strong TSFG.
The vertices are purely TSFNs forn=4 in Figure 13.Figure 13
Strong T-spherical fuzzy graph.Definition 14 (see [43]).
An edgexi,xj in a TSFG Ɠ=N,Ę is known to be a bridge, if by removal of that edge decreases the strength of the connectedness among any pair of vertices in Ɠ.Example 8.
LetƓ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę.
Here,x1,x4 is a bridge.
The vertices shown in Figure14 are purely TSFNs for n=5.Figure 14
TSFG for a bridge.Definition 15 (see [43]).
A vertexxi in a TSFG Ɠ=N,Ę is known to be cut vertex, if the removal of that vertex decreases the strength of the connectedness among any pair of vertices.Example 9.
LetƓ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę.
Here,x1 is a cut vertex.
The vertices are purely TSFNs forn=3 in Figure 15.Figure 15
T-spherical fuzzy graph for cut vertex.


## 3. Operations on T-Spherical Fuzzy Graphs

In this section, the operations on T-spherical fuzzy graph are defined and their results are studied.Definition 18.
The union of a TSFGƓ1=N1,Ę1and Ɠ2=N2,Ę2with N1∩N2=∅ and Ɠ=Ɠ1∪Ɠ2=N1∪N2,Ę1∪Ę2 is defined by(4)s1∪s1′x=s1xifx∈N1−N2s1′xifix∈N2−N1,i1∪i1′x=i1xifx∈N1−N2i1'xifx∈N2−N1,d1∪d1′x=d1xifx∈N1−N2d1′xifx∈N2−N1i,s2∪s2′xixj=s2ijife^˙ij∈Ę1−Ę2s2ij′ife^˙ij∈Ę2−Ę1,i2∪i2′xixj=i2ijife^˙ij∈Ę1−Ę2i2ij′ife^˙ij∈Ę2−Ę1,d2∪d2′xixj=d2ijife^˙ij∈Ę1−Ę2d2ij′ife^˙ij∈Ę2−Ę1,where s1,i1,d1 and s1′,i1′,d1′ represent the vertices of truth membership, abstinence membership, and false membership of Ɠ1 and Ɠ2, respectively, and s2,i2,d2 and s2′,i2′,d2′ represent the edges of truth, abstinence, and false memberships Ɠ1 and Ɠ2, respectively.Example 10.
LetƓ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę. Figures 16–18 are examples of the union of two TSFGs.
The vertices are purely TSFNs forn=5 in Figures 16–18.Figure 16
TSFG-A for union.Figure 17
TSFG-B for union.Figure 18
Union of two TSFGs.Definition 19.
LetƓ1 and Ɠ2 be two TSFGs. Then, the join of Ɠ1 and Ɠ2 is a TSFG, Ɠ=Ɠ1+Ɠ2=N1∪N2,Ę1∪Ę2∪Ę′ defined by: s1+s1′x=s1∪s1′xifx∈N1∪N2, i1+i1′x=i1∪i1′xifx∈N1∪N2, d1+d1′x=d1∪d1′xifx∈N1∪N2 and s2+s2′xixj=s1∪s1′xixjifx∈Ę1∪Ę2=mins1xi,s1′xj if xixj∈Ę′, and i2+i2′xixj=i1∪i1′xixjifx∈Ę1∪Ę2=mini1xi,i1′xj if xixj∈Ę′ and d2+d2′xixj=d1∪d1′xixjifx∈Ę1∪Ę2=maxd1xi,d1′xj if xixj∈Ę′.Theorem 4.
IfƓ1=N1,Ę1 and Ɠ2=N2,Ę2 are two TSFGs, then(i)
Ɠ1+Ɠ2¯≅Ɠ1¯∪Ɠ2¯(ii)
Ɠ1∪Ɠ2¯≅Ɠ1¯+Ɠ2¯Proof.
LetI:N1∪N2⟶N1∪N2 be the identity map. The following steps are calculated to prove (i)(a)
s1+s1′¯xi=s1¯∪s1′¯xi, i1+i1′¯xi=i1¯i∪i1′¯xi, d1+d1′¯xi=d1¯i∪d1′¯xi.(b)
s2+s2′¯xi,xj=s2¯i∪s2′¯xi,xj, ii2+i2′¯xi,xj=i2¯i∪i2′¯xi,xj, d2+d2′¯xi,xj=d2¯i∪d2′¯ixi,xj. Now to prove (a).(i)
s1+s1′¯xi=s1+s1′xi, by definition =s1xiifxi∈N1s1′xiifxi∈N2ii=s1¯xiifxi∈N1s1′¯xiifixi∈N2=s1¯i∪s1′¯xi=ii1+i1′¯,xi=i1+i1′xi=i1xiifxi∈N1i1′xiifxi∈N2i=i1¯ᶌiifxi∈N1i1′¯iᶌiiifxi∈N2=i1¯∪i1′¯ixi, d2+d2′¯xi=d2+d2′xi=d1ᶌiifxi∈N1d1′ᶌiifxi∈N2ii=d1¯xiifxi∈N1d1′¯iixiifxi∈N2=d1¯∪d1′¯xi.(ii)
is2+s2′¯ixi,xj=minis1+s1′xi,is1+s1′xji−is1+s1′xi,xj=mins1+s1′xi,s1+s1′xj−s2∪s2′xi,xjifxi,xj∈Ę1∪Ę2mins1∪s1′xi,is1∪s1′xj−mins1xi,s1′xjifxi,xj∈Ę′=mins1xi,s1′xj−s2xi,xjifxi,xj∈Ę1mins1xi,s1′xj−s2xi,xjifxi,xj∈Ę2mins1ixi,s1′xj−mins1ixi,s1′xjifixi,xj∈Ę′=s2¯xi,xjifxi,xj∈Ę1s2′¯xi,yiifxi,xj∈Ę20ifixi,xj∈Ę=s2¯∪s2′¯xi,xj.i2+i2′¯xi,xj=mini1+i1′xi,i1+i1′xj−i1+i1′xi,xj=mini1+i1′xi,i1+i1′xj−i2∪i2′xi,xjifxi,xj∈Ę1∪Ę2mini1∪i1′xi,i1∪i1′xj−mini1xi,i1′xjifxi,xj∈Ę′=mini1xi,i1′xj−i2xi,xjifxi,xj∈Ę1mini1xi,i1′xj−i2xi,xjifxi,xj∈Ę2mini1ixi,i1′xj−mini1ixi,i1′xjifixi,xj∈Ę′=ı2¯xi,xjifxi,xj∈Ę1ı2′¯xi,xjifxi,xji∈Ę10ifxi,xj∈Ę=ı2¯∪ı2′¯xi,xj.d2+d2′¯xi,xj=maxd1+d1′xi,d1+d1′xj−d1+d1′xi,xjmaxd1+d1′xi,d1+d1′xj−d2∪d2′xi,xjifxi,xj∈Ę1∪Ę2maxd1∪d1′xi,d1∪d1′xj−maxd1xi,d1′xjiifxi,xj∈Ę1=maxd1xi,d1′xj−d2xi,xjifxi,xj∈Ę1maxd1xi,d1′xj−d2xi,xjifxi,xj∈Ę2maxd1xi,d1′xj−maxd1xi,d2′xjifxi,xj∈Ę′=d2¯xi,xjifxi,xj∈Ę1d2′¯xi,xjifxi,xj∈Ę20ifxi,xj∈Ę=d2¯∪d2′¯xi,xj.
Now to prove (ii), we have to show that(a)
s1∪s1′¯xi=s1¯+s1′¯xi, i1∪i1′¯xi=i1¯+i1′¯xi, id1∪d1′¯xi=d1¯+d1′¯xi.(b)
s2∪s2′¯xi,xj=s2¯+s2′¯xi,xj, i2∪i2′¯xi,xj=i2¯+i2′¯xi,xj, id2∪d2′¯xi,xj=d2¯+d2′¯xi,xj.
LetI:N1∪N2⟶N1∪N2 be the identity map.(a)
s1∪s1′¯xi=s1∪s1′xi, by definition =s1xiifixi∈N1s1′xiifixi∈N2i=s1¯xiifixi∈N1s1′¯ixiiifixi∈N2i=s1¯+s1′¯xi, ii1∪i1′¯xi=i1∪i1′xi=i1xiifxi∈N1i1′xiifxi∈N2ii=i1¯Viifxi∈N1i1′¯iViifxi∈N2=i1¯+i1′¯xi, d2∪d2′¯xi=d2∪d2′xi=id1Viifixi∈N1d1′Viifixi∈N2ii=d1¯xiifxi∈N1d1′¯xiifxi∈N2d1¯+d1′¯xi.(b)
s2∪s2′¯xi,xj=mins1∪s1′xi,s1∪s1′xj−s1∪s1′xi,xjmins1xi,s1′xj−s2xi,xjifxi,xj∈Ę1mins1xi,s1′xj−s2xi,xjifxi,xj∈Ę2mins1xi,s1′xj−0ifxi∈N1,xj∈N2=iiis2¯xi,xjifxi,xj∈Ę1s2′¯xi,xjifxi,xj∈Ę2mins1xi,s1′xjifxi∈N1,xj∈N2=s2¯∪s2′¯ifxi,xj∈Ę1orĘ1mins1xi,s1′xjifxi,xj∈Ę′=s2¯i∪s2′¯xi.xj.ı2∪ı2′¯xi,xj=mini1∪i1′xi,ii1∪i1′xj−ii1∪i1′xi,xj=iiiimini1xi,i1′xj−i2xi,xjifxi,xj∈Ę1mini1xi,i1′xj−i2xi,xjifxi,xj∈Ę2mini1xi,i1′xj−0ifixi∈N1,xj∈N2=iiiı2¯xi,xjifxi,xj∈Ę1ı2′¯xi,xjifxi,xj∈Ę2mini1xi,i1′xjifixi∈N1,xj∈N2=ı2¯∪ı2′¯ixi,xjifxi,xj∈Ę1orĘ1mini1xi,i1′xjifxi,xj∈Ę′=ı2¯∪ı2′¯xi,xj.d2∪d2′¯xi,xj=maxd1∪d1′xi,d1∪d1′xj−d1∪d1′xi,xj=maxd1xi,d1′xj−d2xi,xjifxi,xj∈Ę1maxd1xi,d1′xj−d2xi,xjifxi,xj∈Ę2maxd1xi,d1′xj−0ifixi∈N1,xj∈N2=d2¯xi,xjifxi,xj∈Ę1d2′¯xi,xjifxi,xj∈Ę1maxd1xi,d1′xj−0ifxi∈N1,xj∈N2=d2¯∪d2′¯xi,xjifxi,xj∈Ę1orĘ1mind1xi,d1′xjifxi,xj∈Ę′=d2¯∪d2′¯xi,xj.Definition 20.
The Cartesian product of two TSFGsƓ1 and Ɠ2 is denoted and defined by Ɠ=Ɠ1×Ɠ2=N1×N2,Ę1×Ę2i where(a)
s1×s1′u1,u2=mins1u1,s1′u2, for every u1,u2∈N, i1×i′u1,u2=mini1u1,i1′u2 for every u1,u2∈N, and d1×d1′u1,u2=maxd1u1,d1′u2 for every u1,u2∈N.(b)
s1×s1′u,u2u,x2=mins1u,s2u2,x2∀u∈N1, and u2x2∈Ę2i1×i1′u,u2u,x2=mini1u,i2u2,x2∀ui∈N1, and u2x2∈Ę2,d1×d1′u,u2u,x2=maxd1u,d2u2,ix2∀u∈N1, and u2x2∈Ę2. And s2×s2′u1,wx1,w=mins1w,s2u1x1∀w∈N2, u1x1∈Ę1, i2×i2′u1,wx1,w=mini1w,i2u1x1∀w∈N2, u1x1∈Ę1, d2×d2′u1,wx1,w=maxd1w,d2u1x1, ∀w∈N2, u1x1∈Ę1.Example 11.
LetƓ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę. Figures 19–21 present an example of Cartesian product of two TSFGs.
The vertices are purely TSFNs forn=5 in Figures 19–21.Figure 19
TSFG-A for product.Figure 20
TSFG-B for product.Figure 21
Product of two TSFGs.Definition 21.
IfG=G1∘G2=N1×N2,Ę is the composition between two graphs iG1and iG2, where(5)Ę=u,u2u,x2:ui∈N1,andu2x2∈Ę2∪u1,wx1,w:w∈N2,u1x1∈Ę1∪u1,u2x1,x2:u1x1∈Ę1,u2≠x2,then the composition of TSFGs Ɠ1 and Ɠ2Ɠ=Ɠ1∘Ɠ2 is defined by(a)
s1∘s1′u1,u2=mins1u1,s1′u2 for every u1,u2∈N1×N2,i1∘i1′u1,u2=mini1u1,i1′u2 for every u1,u2∈N1×N2, and d1∘d1′u1,u2=mind1u1,d1′u2 for every u1,u2∈N1×N2.(b)
s2∘s2′u,u2u,x2=mins1u,s2u,x2 for every ui∈N1, and u2,x2∈Ę2,i2i2′u,u2u,x2=mini1u,i2u,x2 for every ui∈N1, and u2,x2∈Ę2, d2∘d2′u,u2u,x2=maxd1u,d2u,x2 for every ui∈N1, and u2,x2∈Ę2 And s2∘s2′u1,wx1,w=mins1w,s2u1V1 for every w∈N2, u1x1∈Ę1,i2∘i2′u1,wx1,w=mini1w,i2u1x1for everyw∈N2, u1x1∈Ę1, d2∘d2′u1,wx1,w=maxd1w,d2u1x1 for every w∈V˜2, u1x1∈Ę1. s2∘s2′u1,u2x1,x2=mins1′u2,s1′x2,s2u1,x1 for everyu1,u2x1,x2∈E˜−E˜″,i2∘i2′u1,u2x1,x2=mini1′u2,i1′x2,i2u1,x1 for every u1,u2x1,x2∈Ę−Ę″,d2∘d2′u1,u2x1,x2=maxd1′u2,d1′x2,d2u1,x1 for every u1,u2x1,x2∈Ę−Ę″, where Ę″=u,u2u,x2: u∈N1 for every iu2x2∈Ę2∪u1,wx1,w:w∈N2 for every u1x1∈Ę1.Theorem 5.
IfƓ1=N1,Ę1 and Ɠ2=N2,Ę2 are two strong TSFGs, then Ɠ1∘Ɠ2 is a TSFG.Proof.
Straight forward.Example 12.
LetƓ=N,Ę represent a graph with the collection of vertices N and the collection of edges Ę. Figures 22–24 present an example of composition of two TSFGs.
The vertices shown in Figures22–24 are purely strong TSFNs for n=4.Figure 22
TSFG for composition-A.Figure 23
TSFG for composition-B.Figure 24
Composition of TSFG.Remark 3.
All the definitions and results discussed in Sections3 and 4 for TSFGs are valid for spherical fuzzy graphs (SFGs) if we assume that the vertices and edges are purely SFNs for n=2.


## 4. Shortest Path Problem

There are several problems that have been solved using the concept of graphs in which the shortest path problem is the one which got great attention in recent decades. Finding the shortest path has been always a challenge in different sciences, and several algorithms have been developed so far in which Dijkstra algorithm is of great interest. As demonstrated in introduction section of this paper, this type of problems has been given great importance in various fuzzy algebraic structures, which results in the development of several new approaches. Our aim is to follow the Dijkstra algorithm and apply it to a network of nodes where the path information has been provided in the form of TSFNs.In this section, we assume a network where the shortest path must be computed from source node (SN) to destination node (DN) and the information about path between every two nodes is provided in the form of TSFNs. Usually, the shortest path is the one which is less costly or requires less time or the one on which one must travel less distance between SN and DN. The Dijkstra algorithm in T-spherical fuzzy environment is demonstrated briefly in the following.


### 4.1. T-Spherical Fuzzy Dijkstra Algorithm

The most reasonable approach to find shortest path in a network is to follow Dijkstra algorithm which is the successful algorithm used by many researchers such as [39–42]. The detailed steps of Dijkstra algorithm is for T-spherical fuzzy network are stated as follows.(i)
The source node is marked as permanent node (P). Moreover, it is labelled as0,0,1,−. Therefore, this node is involved in shortest path by default and distance travelled is zero at this stage.(ii)
Compute the labelvi⊕dij,i if j is not a permanent node where j is a node whose path is from node i. Furthermore, if j is labelled as vj,ik through some other node, then replace vj,ik by vi⊕dij,i only if SCvi⊕dij is less than SCvj.(iii)
If all of the nodes are labelled permanently, then the algorithm terminates. Otherwise, choosevr,is having shortest distance vr and repeat Step 2 by setting i=r.(iv)
Using the information of the label, find the shortest path from SN to DN.The flowchart the algorithm is shown in Figure25.Figure 25
Flowchart of modified Dijkstra algorithm for computing the shortest path.Remark 4.
vi⊕dij,i is a label which states that the current location is node i and we travelled a distance vi⊕dij. Further, it is to be noted that the process cannot be continued to a permanent node but can be reversed. For two directly connected adjacent nodes i and j, node i is considered as the predecessor of node j if the path connecting them is directed from i to j.Example 13.
In Figure26, a network is portrayed which is composed of 6 nodes and 8 edges. The aim is to find out the shortest path from SN N1 to DN N2 using modified Dijkstra algorithm.
The list of edges involved in this network is given in Table1.
Now, we apply the modified Dijkstra algorithm and carry out the step-wise computations.Figure 26
T-spherical fuzzy network.Table 1
Weights of edges.
EdgesT-spherical distancesN1,N20.5,0.5,0.7N1,N30.3,0.6,0.8N2,N30.8,0.4,0.8N2,N50.9,0.6,0.8N3,N40.7,0.4,0.3N3,N50.7,0.8,0.9N4,N60.5,0.4,0.8N5,N60.6,0.5,0.3Step 1.
Node 1 is in the shortest path by default, and thus we mark it as a permanent node.Step 2.
Node 1 is connected to two other nodes, and thus there are two ways, i.e., we might move from node 1 to node 3 or from node 1 to node 2. Hence, the list of nodes is given in Table2.
Now we compute the scores by Definition9 of 0.3,0.6,0.8 and 0.5,0.5,0.7.(6)SC0.3,0.6,0.8=0.00245,SC0.5,0.5,0.7=0.022.
As the score of0.3,0.6,0.8 is less than 0.5,0.5,0.7, we mark node 3 as 0.3,0.6,0.8,N1 and label it as permanent.Table 2
List of nodes.
NodesLabelStatusN10,0,1,−PermanentN20.5,0.5,0.7,N1TemporaryN30.3,0.6,0.8,N1TemporaryStep 3.
Again there are two ways to initiate from node 3, i.e., we might move from node 3 to node 5 or from node 3 to node 4. Hence, the list of nodes is given in Table3.
Now we compute the scores of0.54,0.24,0.24 and 0.54,0.48,0.72 as follows:(7)SC0.54,0.24,0.24=0.086,SC0.54,0.48,0.72=0.028.
As the score of0.54,0.48,0.72 is less than0.54,0.24,0.24, we mark node 5 as 0.54,0.48,0.72,N3 and label it as permanent.Table 3
List of nodes.
NodesLabelStatusN10,0,1,−PermanentN20.5,0.5,0.7,N1TemporaryN30.3,0.6,0.8,N1PermanentN40.54,0.24,0.24,N3TemporaryN50.54,0.48,0.72,N3TemporaryStep 4.
The only way out of node 5 leads to node 6. Hence, the list of nodes is given in Table4.
Since there is a single way from node 5 to 6, node 6 is marked as0.37,0.24,0.23,N5 and labelled as permanent.Table 4
List of nodes.
NodesLabelStatusN10,0,1,−PermanentN20.5,0.5,0.7,N1TemporaryN30.3,0.6,0.8,N1PermanentN40.54,0.24,0.24,N3TemporaryN50.54,0.48,0.72,N3PermanentN60.37,0.24,0.23,N5PermanentStep 5.
Nodes 4 and 2 are the temporary nodes left over; therefore, their status is altered to permanent and the following list of nodes is obtained (Table5).Table 5
List of nodes.
NodesLabelStatusN10,0,1,−PermanentN20.5,0.5,0.7,N1PermanentN30.3,0.6,0.8,N1PermanentN40.54,0.24,0.24,N3PermanentN50.54,0.48,0.72,N3PermanentN60.37,0.24,0.23,N5PermanentStep 6.
Table6 implies the following sequence of shortest path from SN to DN, i.e., from node 1 to node 6.
Hence, according to modified Dijkstra algorithm, the shortest path is(8)N1⟶N3⟶N5⟶N6.Table 6
List of nodes.
N60.37,0.24,0.23,N5N50.54,0.48,0.72,N3N30.3,0.6,0.8,N1


#### 4.1.1. Comparative Study

In this section, our aim is to analyse and compare the networks of TSFGs with existing concepts and prove the superiority of T-spherical fuzzy Dijkstra algorithm over existing approaches.We take a network in the environment of IFSs where the information of paths is provided in IFNs. The network presented in Figure27 is based on IFNs as all the values of paths are in IFNs and such information could be very easily converted into TSFNs if we assume the value of i=0. Hence, we can determine the shortest path using the proposed approach.Figure 27
Intuitionistic fuzzy network.Similarly, a network where information is in the form of FNs can also be transformed to a network of TSFNs by assuming the values ofi=d=0. For example, the network in Figure 28 is based on fuzzy information, and hence using the proposed approach of T-spherical fuzzy Dijkstra algorithm, we can easily compute the shortest path from source node to destination node.Figure 28
Fuzzy network.


## 4.1. T-Spherical Fuzzy Dijkstra Algorithm

The most reasonable approach to find shortest path in a network is to follow Dijkstra algorithm which is the successful algorithm used by many researchers such as [39–42]. The detailed steps of Dijkstra algorithm is for T-spherical fuzzy network are stated as follows.(i)
The source node is marked as permanent node (P). Moreover, it is labelled as0,0,1,−. Therefore, this node is involved in shortest path by default and distance travelled is zero at this stage.(ii)
Compute the labelvi⊕dij,i if j is not a permanent node where j is a node whose path is from node i. Furthermore, if j is labelled as vj,ik through some other node, then replace vj,ik by vi⊕dij,i only if SCvi⊕dij is less than SCvj.(iii)
If all of the nodes are labelled permanently, then the algorithm terminates. Otherwise, choosevr,is having shortest distance vr and repeat Step 2 by setting i=r.(iv)
Using the information of the label, find the shortest path from SN to DN.The flowchart the algorithm is shown in Figure25.Figure 25
Flowchart of modified Dijkstra algorithm for computing the shortest path.Remark 4.
vi⊕dij,i is a label which states that the current location is node i and we travelled a distance vi⊕dij. Further, it is to be noted that the process cannot be continued to a permanent node but can be reversed. For two directly connected adjacent nodes i and j, node i is considered as the predecessor of node j if the path connecting them is directed from i to j.Example 13.
In Figure26, a network is portrayed which is composed of 6 nodes and 8 edges. The aim is to find out the shortest path from SN N1 to DN N2 using modified Dijkstra algorithm.
The list of edges involved in this network is given in Table1.
Now, we apply the modified Dijkstra algorithm and carry out the step-wise computations.Figure 26
T-spherical fuzzy network.Table 1
Weights of edges.
EdgesT-spherical distancesN1,N20.5,0.5,0.7N1,N30.3,0.6,0.8N2,N30.8,0.4,0.8N2,N50.9,0.6,0.8N3,N40.7,0.4,0.3N3,N50.7,0.8,0.9N4,N60.5,0.4,0.8N5,N60.6,0.5,0.3Step 1.
Node 1 is in the shortest path by default, and thus we mark it as a permanent node.Step 2.
Node 1 is connected to two other nodes, and thus there are two ways, i.e., we might move from node 1 to node 3 or from node 1 to node 2. Hence, the list of nodes is given in Table2.
Now we compute the scores by Definition9 of 0.3,0.6,0.8 and 0.5,0.5,0.7.(6)SC0.3,0.6,0.8=0.00245,SC0.5,0.5,0.7=0.022.
As the score of0.3,0.6,0.8 is less than 0.5,0.5,0.7, we mark node 3 as 0.3,0.6,0.8,N1 and label it as permanent.Table 2
List of nodes.
NodesLabelStatusN10,0,1,−PermanentN20.5,0.5,0.7,N1TemporaryN30.3,0.6,0.8,N1TemporaryStep 3.
Again there are two ways to initiate from node 3, i.e., we might move from node 3 to node 5 or from node 3 to node 4. Hence, the list of nodes is given in Table3.
Now we compute the scores of0.54,0.24,0.24 and 0.54,0.48,0.72 as follows:(7)SC0.54,0.24,0.24=0.086,SC0.54,0.48,0.72=0.028.
As the score of0.54,0.48,0.72 is less than0.54,0.24,0.24, we mark node 5 as 0.54,0.48,0.72,N3 and label it as permanent.Table 3
List of nodes.
NodesLabelStatusN10,0,1,−PermanentN20.5,0.5,0.7,N1TemporaryN30.3,0.6,0.8,N1PermanentN40.54,0.24,0.24,N3TemporaryN50.54,0.48,0.72,N3TemporaryStep 4.
The only way out of node 5 leads to node 6. Hence, the list of nodes is given in Table4.
Since there is a single way from node 5 to 6, node 6 is marked as0.37,0.24,0.23,N5 and labelled as permanent.Table 4
List of nodes.
NodesLabelStatusN10,0,1,−PermanentN20.5,0.5,0.7,N1TemporaryN30.3,0.6,0.8,N1PermanentN40.54,0.24,0.24,N3TemporaryN50.54,0.48,0.72,N3PermanentN60.37,0.24,0.23,N5PermanentStep 5.
Nodes 4 and 2 are the temporary nodes left over; therefore, their status is altered to permanent and the following list of nodes is obtained (Table5).Table 5
List of nodes.
NodesLabelStatusN10,0,1,−PermanentN20.5,0.5,0.7,N1PermanentN30.3,0.6,0.8,N1PermanentN40.54,0.24,0.24,N3PermanentN50.54,0.48,0.72,N3PermanentN60.37,0.24,0.23,N5PermanentStep 6.
Table6 implies the following sequence of shortest path from SN to DN, i.e., from node 1 to node 6.
Hence, according to modified Dijkstra algorithm, the shortest path is(8)N1⟶N3⟶N5⟶N6.Table 6
List of nodes.
N60.37,0.24,0.23,N5N50.54,0.48,0.72,N3N30.3,0.6,0.8,N1


### 4.1.1. Comparative Study

In this section, our aim is to analyse and compare the networks of TSFGs with existing concepts and prove the superiority of T-spherical fuzzy Dijkstra algorithm over existing approaches.We take a network in the environment of IFSs where the information of paths is provided in IFNs. The network presented in Figure27 is based on IFNs as all the values of paths are in IFNs and such information could be very easily converted into TSFNs if we assume the value of i=0. Hence, we can determine the shortest path using the proposed approach.Figure 27
Intuitionistic fuzzy network.Similarly, a network where information is in the form of FNs can also be transformed to a network of TSFNs by assuming the values ofi=d=0. For example, the network in Figure 28 is based on fuzzy information, and hence using the proposed approach of T-spherical fuzzy Dijkstra algorithm, we can easily compute the shortest path from source node to destination node.Figure 28
Fuzzy network.


## 4.1.1. Comparative Study

In this section, our aim is to analyse and compare the networks of TSFGs with existing concepts and prove the superiority of T-spherical fuzzy Dijkstra algorithm over existing approaches.We take a network in the environment of IFSs where the information of paths is provided in IFNs. The network presented in Figure27 is based on IFNs as all the values of paths are in IFNs and such information could be very easily converted into TSFNs if we assume the value of i=0. Hence, we can determine the shortest path using the proposed approach.Figure 27
Intuitionistic fuzzy network.Similarly, a network where information is in the form of FNs can also be transformed to a network of TSFNs by assuming the values ofi=d=0. For example, the network in Figure 28 is based on fuzzy information, and hence using the proposed approach of T-spherical fuzzy Dijkstra algorithm, we can easily compute the shortest path from source node to destination node.Figure 28
Fuzzy network.


## 5. Conclusion

In this paper, the concept of TSFG is introduced based on the novel theory of TSFSs. In view of the novelty of TSFSs, the importance of TSFGs is elaborated and it is discussed that TSFGs are generalizations of IFGs and PFGs and can be applicable in those situations where the frameworks of IFG and PFG failed to be applied. Some very basic graph theoretic terms like complement of TSFGs, T-spherical fuzzy subgraph, degree of vertices in TSFGs, strength of TSFGs, and bridges in TSFGs are defined. A study of operations of TSFGs is also established and related results are studied. The famous Dijkstra algorithm for TSFGs has been developed and the shortest path in a network of TSFGs has been solved. The main benefit of the proposed work is that it could be applied in the conditions that are handled by using the concepts of IFG or PFG, but these structures are incapable of handling the information given in the T-spherical fuzzy environment. In the near future, the framework of TSFGs could prove to be very useful tool that can be applied in the traffic signal problems, optimization in networks, and other problems of computer sciences and engineering. Additionally, the proposed work can be extended to interval-valued and cubic-valued frameworks that will give rise to much stronger and interesting structures with extended range of applications.



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*Source: 7946936-2021-09-27.xml*