AFS Advances in Fuzzy Systems 1687-711X 1687-7101 Hindawi 10.1155/2019/5213020 5213020 Research Article Fuzzy Chromatic Polynomial of Fuzzy Graphs with Crisp and Fuzzy Vertices Using α-Cuts http://orcid.org/0000-0001-8096-0893 Abebe Ashebo Mamo 1 http://orcid.org/0000-0003-3094-1170 Repalle V. N. Srinivasa Rao 1 Dvorák Antonin Department of Mathematics Wollega University Nekemte Ethiopia wollegauniversity.edu.et 2019 252019 2019 24 01 2019 28 03 2019 02 04 2019 252019 2019 Copyright © 2019 Mamo Abebe Ashebo and V. N. Srinivasa Rao Repalle. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Coloring of fuzzy graphs has many real life applications in combinatorial optimization problems like traffic light system, exam scheduling, register allocation, etc. In this paper, the concept of fuzzy chromatic polynomial of fuzzy graph is introduced and defined based on α-cuts of fuzzy graph. Two different types of fuzziness to fuzzy graph are considered in the paper. The first type was fuzzy graph with crisp vertex set and fuzzy edge set and the second type was fuzzy graph with fuzzy vertex set and fuzzy edge set. Depending on this, the fuzzy chromatic polynomials for some fuzzy graphs are discussed. Some interesting remarks on fuzzy chromatic polynomial of fuzzy graphs have been derived. Further, some results related to the concept are proved. Lastly, fuzzy chromatic polynomials for complete fuzzy graphs and fuzzy cycles are studied and some results are obtained.

1. Introduction

Nowadays, many real world problems cannot be properly modeled by a crisp graph theory, since the problems contain uncertain information. The fuzzy set theory, anticipated by Zadeh , is used to handle the phenomena of uncertainty in real life situation. A lot of works have been done in fuzzy shortest path problems using type 1 fuzzy set in . Dey et al.  introduced interval type 2 fuzzy set in the fuzzy shortest path problems. Recently, in , the authors proposed a genetic algorithm for solving fuzzy shortest path problem with interval type 2 fuzzy arc lengths. Some researchers also used the fuzzy set theory to touch the uncertainty in crisp graphs. Kaufmann  proposed the first definition of fuzzy graph in 1973, based on Zadeh’s fuzzy relations. Later, Rosenfeld  introduced another elaborated definition of fuzzy graph with fuzzy vertex set and fuzzy edge set in 1975. He developed the theory of fuzzy graph. After that, Bhattacharya  has established some connectivity concepts regarding fuzzy cut nodes and fuzzy bridges. Bhutani  has studied automorphisms on fuzzy graphs and certain properties of complete fuzzy graphs. Also, Mordeson and Nair  introduced cycles and cocycles of fuzzy graphs. Several authors including Sunitha and Vijayakumar [13, 14], Bhutani and Rosenfeld , Mathew and Sunitha , Akram , and Akram and Dudek  have introduced numerous concepts in fuzzy graphs. Fuzzy graph theory has several applications in various fields like clustering analysis, database theory, network analysis, information theory, etc. .

Coloring of fuzzy graphs plays a vital role in theory and practical applications. It is mainly studied in combinatorial optimization problems like traffic light control, exam scheduling, register allocation, etc. . Fuzzy coloring of a fuzzy graph was defined by authors Eslahchi and Onagh in 2004 and later developed by them as fuzzy vertex coloring  in 2006. Another approach of coloring of fuzzy graphs was introduced by Munoz et al. . The authors have defined the chromatic number of a fuzzy graph. Incorporating the above two approaches of coloring of fuzzy graph, Kishore and Sunitha  introduced chromatic number of fuzzy graphs and developed algorithm. Dey and Pal  introduced the vertex coloring of a fuzzy graph using α-cuts. In , they have used the vertex coloring of fuzzy graph to classify the accidental zone of a traffic control. Further, in , the authors proposed genetic algorithm to find the robust solutions for fuzzy robust coloring problem. The authors, Ananthanarayanan and Lavanya , introduced fuzzy chromatic number and fuzzy total chromatic number of a fuzzy graph using α-cuts. Rosyida et al.  proposed a new approach to determine fuzzy chromatic number of fuzzy graph through its δ-chromatic number. Samanta et al.  also introduced a new concept for coloring of fuzzy graphs using fuzzy color. Lately, Dey et al.  introduced the concept of vertex and edge coloring of vague graphs which are the generalized structure of fuzzy graphs.

In the literature, to the best of our knowledge, there is no study on the fuzzy chromatic polynomial of fuzzy graphs. Therefore, in this paper, we consider the chromatic polynomial in fuzzy graph, called fuzzy chromatic polynomial of fuzzy graph. Based on this, we define the concept of fuzzy chromatic polynomial of a fuzzy graph using α-cuts of fuzzy graph. Also, we determine the fuzzy chromatic polynomial for fuzzy graphs with crisp and fuzzy vertex set. To determine the fuzzy chromatic polynomial, the classical method for computing the chromatic polynomial of crisp graph is used. Next, we derive some interesting remarks on fuzzy chromatic polynomial of fuzzy graph with crisp and fuzzy vertices. Further, we prove more elegant results on fuzzy chromatic polynomial of fuzzy graphs. Finally, we study a fuzzy chromatic polynomial for complete fuzzy graphs and fuzzy cycles.

The rest of paper is organized as follows. In Section 2, some basic definitions and elementary concepts of fuzzy set, fuzzy graph, and coloring of fuzzy graphs are reviewed. In Section 3, fuzzy chromatic polynomial of a fuzzy graph using α-cut of fuzzy graph is defined. Also, fuzzy chromatic polynomials for fuzzy graphs with crisp and fuzzy vertices are determined. In Section 4, more results on fuzzy chromatic polynomials are proved. Furthermore, fuzzy chromatic polynomial for complete fuzzy graphs and fuzzy cycles are studied. Finally, the paper is concluded in Section 5.

2. Preliminaries

In this section, some basic aspects that are necessary for this paper are included. These preliminaries are given in three subsections.

2.1. Basic Definitions and Concepts on Vertex Coloring and Chromatic Polynomial

In this subsection some basic definitions and concepts of vertex coloring and chromatic polynomials are reviewed [30, 31].

Definition 1.

Let G    =  (V,E) be a graph. A vertex-coloring of G is an assignment of a color to each of the vertices of G in such a way that adjacent vertices are assigned different colors. If the colors are chosen from a set of k colors, then the vertex-coloring is called a k-vertex-coloring, abbreviated to k-coloring, whether or not all k colors are used.

Definition 2.

If G has a k-coloring, then G is said to be k-colorable.

Definition 3.

The smallest k, such that G is k-colorable, is called the chromatic number of G, denoted by χ(G).

Definition 4.

Let G be a simple graph. The chromatic polynomial of G is the number of ways we can achieve a proper coloring on the vertices of G with the given k colors and it is denoted by P(G,k). It is a monic polynomial in k with integer coefficients, whose degree is the number of vertices of G.

If we are given an arbitrary simple graph, it is usually difficult to obtain its chromatic polynomial by examining the structure of a graph (by inspection). The following theorem gives us a systematic method for obtaining the chromatic polynomial of a simple graph in terms of the chromatic polynomial of null graphs.

Theorem 5.

Let G be a simple graph, and let G-e and G/e be the graphs obtained from G by deleting and contracting an edge e. Then(1)PG,k=PG-e,k-PG/e,k

2.2. Basic Definitions on Fuzzy Set and Fuzzy Graphs

In this subsection, some basic definitions on fuzzy set and fuzzy graphs are reviewed [1, 9, 19, 32].

Definition 6.

A fuzzy set A defined on a nonempty set X is the familyA=x,μAxxX, where μA:XI is the membership function. In classical fuzzy set theory the set I is usually defined as the interval [0,1] such that(2)μAx=1,xA0,xAIt takes any intermediate value between 0 and 1 represents the degree in which xA. The set I could be discrete set of the form I={0,1,,k} where μA(x)<μA(x) indicates that the degree of membership of x to A is lower than the degree of membership of x.

Definition 7.

α -cut set of fuzzy set A is defined as Aα is made up of members whose membership is not less than α, Aα=xXμA(x)α. α-cut set of fuzzy set is crisp set.

Definition 8.

A fuzzy graph G=(V,σ,μ) is an algebraic structure of nonempty set V together with a pair of functions σ:V[0,1] and μ:V×V[0,1] such that for all u,vV, μ(u,v)σ(u)σ(v) and μ is a symmetric fuzzy relation on σ. Here σ(u) and μ(u,v) represent the membership values of the vertex u and of the edge (u,v) in G, respectively.

In this paper, we denote uv=min{u,v} and uv=max{u,v}. Here, we considered fuzzy graph G is simple (with no loops and parallel edges), finite, and undirected. μ is reflective (that is, μv,v=σv, for all vV) and symmetric (that is, μu,v=μv,u, for all (u,v)E).

Note that a fuzzy graph is a generalization of crisp graph in which σ(v)=1 for all vV and μu,v=1 if u,vE and μu,v=0 if u,vE. So, all the crisp graphs are fuzzy graphs but all fuzzy graphs are not crisp graphs.

Definition 9.

The fuzzy graph G=(V,σ,μ) is called a fuzzy subgraph of G if for each two elements u,vV, we have σ(u)σ(u) and μ(u,v)μ(u,v).

Definition 10.

The fuzzy graph G=(V,σ,μ) is called connected if, for every two elements u,vV, there exists a sequence of elements u0,u1,,um such that u0=u, um=v, and μ(ui,ui+1)>0, (0im-1).

Definition 11.

For any fuzzy graph G=(V,σ,μ), let L={σ(u)>0uV}{μ(u,v)>0uv,u,vV} with k elements. Now assume L={α1,α2,,αk} such that α1<α2<<αk. The sequence {α1,α2,,αk} and the set L are called the fundamental sequence and the fundamental set (or level set) of G, respectively.

Definition 12.

The underlying crisp graph G=(V,E) of a fuzzy graph G=(V,σ,μ) is such that V=uVσ(u)>0 and E=u,vμ(u,v)>0.

Definition 13.

For α0,1, α-cut graph of a fuzzy graph G=(V,σ,μ) is a crisp graph Gα=(Vα,Eα) such that Vα=uVσ(u)α and Eα=u,vμ(u,v)α. It is obvious that a fuzzy graph will have a finite number of different α-cuts.

2.3. Basic Definitions in Fuzzy Coloring of Fuzzy Graphs

The concept of chromatic number of fuzzy graph was introduced by Munoz et al. . The authors considered fuzzy graphs with crisp vertex set, that is, fuzzy graphs, for which σ(v)=1 for all vV and edges with membership degree in [0,1].

Definition 14 (see [<xref ref-type="bibr" rid="B22">22</xref>]).

If G=(V,μ) is such a fuzzy graph where V={1,2,3...n} and μ is a fuzzy number on the set of all subsets of V×V, assume I=A{0}, where A={α1<α2<<αk} is the fundamental set (level set) of G. For each αI, Gα denote the crisp graph Gα=(V,Eα), where Eα=ij1i<jn,μ(i,j)α} and χα=χ(Gα) denote the chromatic number of crisp graph Gα.

By this definition the chromatic number of fuzzy graphs G is the fuzzy number χ(G)={(i,v(i))/iX}, where vi=maxαIiAα and Aα={1,2,3,χα}.

Later Eslahchi and Onagh  introduced fuzzy vertex coloring of fuzzy graph. They defined fuzzy chromatic number as the least value of k for which the fuzzy graph G has k-fuzzy coloring as follows.

Definition 15 (see [<xref ref-type="bibr" rid="B21">21</xref>]).

A family Γ={γ1,γ2,,γk} of fuzzy sets on a set V is called a k-fuzzy coloring of G=(V,σ,μ) if

Γ=σ,

γiγj=0,

for every strong edge (x,y) (i.e., μ(x,y)>0 ) of G, min{γi(x),γi(y)}=0 (1ik).

Definition 16 (see [<xref ref-type="bibr" rid="B21">21</xref>]).

The minimum number k for which there exists a k-fuzzy coloring is called the fuzzy chromatic number of G, denoted as χfG.

Incorporating the features of the above two definitions, Kishore and Sunitha  modified the chromatic number χ(G) of fuzzy graph as follows.

Definition 17 (see [<xref ref-type="bibr" rid="B23">23</xref>]).

For each αϵI, Gα denote the crisp graph Gα=(σα,μα) and χα=χ(Gα) denote the chromatic number of crisp graph Gα. The chromatic number of fuzzy graph G=(V,σ,μ) is the number χG=max{χGααA}.

3. Fuzzy Chromatic Polynomial of a Fuzzy Graph

A fuzzy chromatic polynomial is a polynomial which is associated with the fuzzy coloring of fuzzy graphs. Therefore, chromatic polynomial in fuzzy graph is called fuzzy chromatic polynomial of fuzzy graph. In this section, we define the concept of fuzzy chromatic polynomial of a fuzzy graph based on α-cuts of fuzzy graph which are crisp graphs. Furthermore, we determine the fuzzy chromatic polynomials for some fuzzy graphs with crisp and fuzzy vertices.

Crisp vertex coloring and the chromatic polynomial of α-cut graph of the fuzzy graph are defined as follows.

Definition 18.

Let G=(V,σ,μ) be a fuzzy graph and Gα denote α-cut graph of the fuzzy graph G which is a crisp graph Gα=(Vα,Eα), α0,1. A function Cαk:Vα{1,2,,k}, kN is called a k-coloring (crisp vertex coloring) of Gα if Cαk(vi)Cαk(vj) whenever the vertices vi and vj are adjacent in Gα.

Definition 19.

The number of distinct k-coloring on the vertices of Gα is called the chromatic polynomial of Gα. It is denoted by P(Gα,k).

Let GααI be the family of α-cuts sets of G, where the α-cut of a fuzzy graph is the crisp graph Gα. Hence, any crisp k-coloring Cαk can be defined on the vertex set of Gα. The k-coloring function of the fuzzy graph G is defined through this sequence. For each αI, let the fuzzy chromatic polynomial of G be defined through a monotone family of sets.

Fuzzy chromatic polynomial of a fuzzy graph is defined as follows.

Definition 20.

Let G be a fuzzy graph. The fuzzy chromatic polynomial of G is defined as the chromatic polynomial of its crisp graphs Gα, for αI. It is denoted by Pαf(G,k).

That is, PαfG,k=P(Gα,k), αI.

3.1. Fuzzy Chromatic Polynomial of Fuzzy Graph with Crisp Vertices

In this subsection, we present fuzzy chromatic polynomial of fuzzy graphs with crisp vertices and fuzzy edges.

A fuzzy graph G with crisp vertices and fuzzy edges, and α-cut graph of G are defined as follows.

Definition 21.

A fuzzy graph is defined as a pair G=(V,μ) such that

V is the crisp set of vertices (that is, σu=1,uV);

the function μ:VxV[0,1] is defined by μu,vσ(u)σ(v), for all u,vV.

Definition 22.

Let G=(V,μ) be a fuzzy graph. For αI, α-cut graph of the fuzzy graph G is defined as the crisp graph Gα=V,Eα, where Eα=u,v,u,vVμ(u,v)α.

Example 23.

Consider the fuzzy graph G with crisp vertices and fuzzy edges in Figure 1.

The fuzzy graph G with crisp vertices and fuzzy edges.

In G, we consider I={0,0.2,0.3,0.5,0.8,1}; for each αI, we have a crisp graph Gα and its chromatic polynomial which is the fuzzy chromatic polynomial of the fuzzy graph G is obtained (see Figure 2). (The integers in the brackets denote the number of ways of coloring the vertices.)

Different fuzzy chromatic polynomials of the fuzzy graph G in Example 23.

Remark 24.

The fuzzy chromatic polynomial depends on the values of α, which means the fuzzy chromatic polynomial varies for the same fuzzy graph G for different values of α.

For the fuzzy graph G in Example 23, the fuzzy chromatic polynomial varies for different values of α as shown below: (3)PαfG,k=kk-1k-2k-3,α=0kk-1k-22,α=0.2kk-12k-2,α=0.3kk-13,α=0.5k2k-12,α=0.8k4,α=1

Remark 25.

The fuzzy chromatic polynomial of a fuzzy graph does not need to be decreased when the value of α increases. Let us consider α1=0.2, α2=0.3, α3=0.5, α4=0.8, and α5=1 for the fuzzy graph G in Example 23, but PαifG,k, kχαi, where χαi is the chromatic number of the crisp graph Gαi i=1,2,3,4,5 is not decreased as αi increases. For instance, Pα1fG,χα1=6, Pα2fG,χα2=12, Pα3fG,χα3=2, Pα4fG,χα4=4, and Pα5fG,χα5=1.

Remark 26.

Let G be a fuzzy graph with n vertices. Then for G with crisp vertices and fuzzy edges and for α=1, PαfG,k is not always P(Nn,k), where Nn is the null crisp graph with n vertices which is clear from the following example.

Example 27.

Consider the fuzzy graph G given in Figure 3(a). In Figure 3(a), for α=1, Gα is a crisp graph in Figure 3(b). But Gα is not a null crisp graph with three vertices. Thus, PαfG,kPN3,k.

A fuzzy graph G

A crisp graph Gα for α=1

The following result gives the degree of fuzzy chromatic polynomial of fuzzy graph with crisp vertices and fuzzy edges are equal to the number of vertices in crisp vertex set.

Lemma 28.

Let G=(V,μ) be a fuzzy graph with crisp vertices and fuzzy edges. Then the degree of PαfG,k=V, for all αI.

Proof.

Let G=(V,μ) be a fuzzy graph with crisp vertices and fuzzy edges. Now, Gα=(V,Eα), where Eα=u,vμ(u,v)α. We know that the degree of the chromatic polynomial of a crisp graph Gα is the number of vertices of Gα. That is, the degree of PGα,k=V. By Definition 20, we have PαfG,k=P(Gα,k), for αI. From this it follows that the degree of PαfG,k is equal to the degree of PGα,k. Therefore, PαfG,k=V.

3.2. Fuzzy Chromatic Polynomial of Fuzzy Graph with Fuzzy Vertices

In Section 3.1, we introduced the fuzzy chromatic polynomial of fuzzy graph G=(V,μ), where V is crisp set vertices and E is fuzzy set of edges. In this subsection, we present fuzzy chromatic polynomial of fuzzy graphs with fuzzy vertex set and fuzzy edge set. Here, we use Definition 8 for fuzzy graph and Definition 13 for α-cut graph which are presented in Section 2.

Example 29.

Consider a fuzzy graph G with fuzzy vertex set and fuzzy edge set in Figure 4

A fuzzy graph G with fuzzy vertices and fuzzy edges.

In G, we consider I={0,0.2,0.3,0.4,0.6,0.8}; for each αI, we have a crisp graph Gα and its chromatic polynomial which is the fuzzy chromatic polynomial of the fuzzy graph G is obtained (see Figure 5). (The integers in the brackets denote the number of ways of coloring the vertices.)

Different fuzzy chromatic polynomials of the fuzzy graph G in Example 29.

Remark 30.

The fuzzy chromatic polynomial varies for the same fuzzy graph G for different values of α. It is clear from Example 29. Here, the fuzzy chromatic polynomial of the fuzzy graph in Example 29 is given below. (4)PαfG,k=kk-1k-2k-3k-4,α=0kk-1k-23,α=0.2k2k-13,α=0.3k5,α=0.4k3,α=0.6k,α=0.8

Remark 31.

For fuzzy graph G with fuzzy vertex set and fuzzy edge set, the degree of the fuzzy chromatic polynomial of G is not always equal to the number of vertices of G. It can be easily seen from the fuzzy chromatic polynomial of the fuzzy graph in Example 29.

For instance, for α=0.4, the degree of PαfG,k=5 which is the number of vertices of G, but for α=0.6, the degree of PαfG,k=35; also for α=0.8, the degree of PαfG,k=15.

Remark 32.

The fuzzy chromatic polynomial of fuzzy graph G with fuzzy vertex set and fuzzy edge set, PαfG,k,kχα will be decreased when the value of α will increase, since in fuzzy graph with fuzzy vertices and fuzzy edges, both Vα and Eα decrease as α increases (see Table 1).

The values of α, Vα, Eα, χα, and PαfG,k, kχα for the fuzzy graph G in Example 33.

α V α E α χ α P α f G , k P α f G , χ α P α f G , χ α + 1    P α f G , χ α + 2          P α f G , χ α + 3
0 3 3 3 k(k-1)(k-2) 6 24 60 120
0.1 3 3 3 k(k-1)(k-2) 6 24 60 120
0.5 3 2 2 k(k-1)2 2 12 36 80
0.7 2 1 2 k(k-1) 2 6 12 20
0.8 2 0 1 k2 1 4 9 16
1 1 0 1 k 1 2 3 4
Example 33.

Consider a fuzzy graph G with fuzzy vertex set and fuzzy edge set in Figure 6.

A fuzzy graph G.

By routine computation, the fuzzy chromatic polynomial of the fuzzy graph G in Figure 6 is obtained as(5)PαfG,k=kk-1k-2,α=0kk-1k-2,α=0.1kk-12,α=0.5kk-1,α=0.7k2,α=0.8k,α=1

Table 1 shows that the fuzzy chromatic polynomial of the fuzzy graph G in Example 33 deceases as α increases (see column 1 and columns 6-9 of Table 1). Moreover, both the number of vertices and the number of edges of α-cut graph of the fuzzy graph decrease as α increases (see column 1 and columns 2 and 3 of Table 1).

4. Main Results

In this section, we prove some relevant results on fuzzy chromatic polynomial for fuzzy graphs which are discussed in Section 3. Moreover, the fuzzy chromatic polynomial of complete fuzzy graph and the fuzzy chromatic polynomial of fuzzy graphs which are cycles and fuzzy cycles are studied.

4.1. <italic>α</italic>-Cut Graph and Fuzzy Graph

The relations among different α-cuts of a fuzzy graph have been established as follows.

Lemma 34.

Let G be a fuzzy graph. If 0αβ1, then the following are true.

VαVβ

EαEβ

Proof.

Let G=(V,σ,μ) be a fuzzy graph and 0αβ1. Now, Gα=(Vα,Eα), where Vα=uVσ(u)α and Eα=u,vμ(u,v)α. Also, Gβ=(Vβ,Eβ), where Vβ=uVσ(u)β and Eβ=u,vμ(u,v)β. Since 0αβ1, we have VβVα and EβEα. From this, results (i) and (ii) hold.

Theorem 35.

Let G be a fuzzy graph. If 0αβ1, then Gβ is a subgraph of Gα.

Proof.

Let G=(V,σ,μ) be a fuzzy graph and 0αβ1. Now, Gα=(Vα,Eα), where Vα=uVσ(u)α and Eα=u,vμ(u,v)α. Also, Gβ=(Vβ,Eβ), where Vβ=uVσ(u)β and Eβ=u,vμ(u,v)β. From Lemma 34, we get VβVα and EβEα. That means, for any element uVβ, uVα and for any element (u,v)Eβ, (u,v)Eα. Hence, Gβ is a subgraph of Gα.

Theorem 36.

Let G be a fuzzy graph. If 0αβ1, then deg PGα,k deg PGβ,k.

Proof.

Let G=(V,σ,μ) be a fuzzy graph and 0αβ1. Now, Gα=(Vα,Eα), where Vα=uVσ(u)α and Eα=u,vμ(u,v)α. Also, Gβ=(Vβ,Eβ), where Vβ=uVσ(u)β and Eβ=u,vμ(u,v)β. We know that the degree of the chromatic polynomial of a crisp graph is the number of vertices in the crisp graph. Therefore, the degree of PGα,k=Vα and the degree of PGβ,k=Vβ. By Lemma 34(i), we have VαVβ. Therefore, deg PGα,k=VαVβ= deg PGβ,k.

The relations between the α-cut graph of a fuzzy graph and the value of α=0, the fuzzy chromatic polynomial of a fuzzy graph, and the chromatic polynomial of corresponding complete crisp graph can be determined below.

Lemma 37.

Let G be a fuzzy graph with n vertices and Gα be α-cut of G. Then if α=0, then Gα is a complete crisp graph with n vertices.

Proof.

Let G=(V,σ,μ) be a fuzzy graph with n vertices and α=0. Now, G0=(V0,E0), where V0=uVσ(u)0 and E0=u,vμ(u,v)0. Here, V0 consists of all the vertices in V of G. Similarly, E0 consists of all the edges in E and all the edges not in E of G. This shows that all the vertices in V0 of G0 are adjacent to each other. Therefore, G0 is a complete crisp graph of n vertices. This completes the proof.

Remark 38.

For α-cut of the fuzzy graph G with n vertices, Gα=Kn if α=0.

Theorem 39.

Let G be a fuzzy graph with n vertices and Gα be α-cut of G. Then if α=0, (6)PαfG,k=PKn,k

Proof.

Let G=(V,σ,μ) be a fuzzy graph with n vertices and α=0. Now, Gα=(Vα,Eα), where Vα=uVσ(u)α and Eα=u,vμ(u,v)α. Since G is fuzzy graph. Then by Definition 20 and Remark 38, the result holds.

The relation between fuzzy chromatic polynomial of a fuzzy graph and the chromatic polynomial of corresponding underlying crisp graph is established below.

Theorem 40.

Let G be a fuzzy graph and G be its underlying crisp graph. If α=minL, where L is the level set or fundamental set of G, then PαfG,k=P(G,k).

Proof.

Let G=(V,σ,μ) be a fuzzy graph and let G=(V,E) be the underlying crisp graph of G, where V=uVσ(u)>0 and E=u,v,u,vVμ(u,v)>0. This means that V=V and E consists of all the edges in E. Let L={α1,α2,...,αk} be a level set or fundamental set of G. Put α=minL; now Gα=(Vα,Eα), where Vα=uVσ(u)α and Eα=u,vμ(u,v)α. Since α is the minimum value in the level set L, Vα consists of all the vertices of V and Eα consists of all the edges in E. Therefore, Gα=G. Since G is fuzzy graph, then, by Definition 20, we have PαfG,k=PGα,k=PG,k.

The following is an important theorem for computing the fuzzy chromatic polynomial of a fuzzy graph explicitly.

Theorem 41.

Let G be a fuzzy graph with n vertices and G be its underlying crisp graph. If αI=L{0} and β=minL, then (7)PαfG,k=PKn,kifα=0PG,kifα=βPGα,kifβ<α1where Kn is a complete crisp graph with n vertices.

Proof.

Let G=(V,σ,μ) be a fuzzy graph with n vertices and let G=(V,E) be the underlying crisp graph of G, where V=uVσ(u)>0 and E=u,v,u,vVμ(u,v)>0. For αI, now Gα=(Vα,Eα), where Vα=uVσ(u)α and Eα=u,vμ(u,v)α. Let us prove the theorem by considering three cases.

Case 1. Let α=0. Then by Theorem 39, the fuzzy chromatic polynomial of the fuzzy graph G is the chromatic polynomial of a complete crisp graph with n vertices, Kn. That is,(8)PαfG,k=PKn,k

Case 2. Let α=β, where β=minL. Then by Theorem 40, the fuzzy chromatic polynomial of the fuzzy graph G is the chromatic polynomial of its underlying crisp graph G. That is, (9)PαfG,k=PG,k

Case 3. Let β<α1; since α is in I, then by Definition 20PαfG,k=PGα,k. This completes the proof.

The following result shows that the degree of fuzzy chromatic polynomial of a fuzzy graph is less than or equal to the number of vertices of the fuzzy graph. It is a significant difference from crisp graph theory.

Theorem 42.

Let G=(V,σ,μ) be a fuzzy graph. Then the degree of PαfG,kV,αI.

Proof.

Let us prove the theorem by considering two cases.

Case 1. Let G=(V,μ) be a fuzzy graph with crisp vertices and fuzzy edges. By Lemma 28, the degree of PαfG,k=V,αI. Therefore, the result holds.

Case 2. Let G=(V,σ,μ) be a fuzzy graph with fuzzy vertex set and fuzzy edge set. Now, Gα=(Vα,Eα), where Vα=uVσ(u)α and Eα=u,vμ(u,v)α. We know that the degree of the chromatic polynomial of a crisp graph Gα is the number of vertices of Gα. That is, the degree of PGα,k=Vα. But for all αI, VαV, since Vα V. Then by Definition 20, we have PαfG,k=PGα,k, for αI. Therefore, the degree of PαfG,k = the degree of PGα,k=VαV. This implies that PαfG,kV,αI. Hence, from Case 1 and Case 2, we conclude that, for any fuzzy graph G, the degree of PαfG,kV, αI.

4.2. Complete Fuzzy Graph and Fuzzy Cycle

In 1989, Bhutani  introduced the concept of a complete fuzzy graph as follows.

Definition 43 (see [<xref ref-type="bibr" rid="B11">11</xref>]).

A complete fuzzy graph is a fuzzy graph G=(V,σ,μ) such that μu,v=σ(u)σ(v) for all u,vV.

The following result gives the underlying crisp graph of a complete fuzzy graph is a complete crisp graph.

Lemma 44.

Let G be a complete fuzzy graph and G be its underlying crisp graph. Then G is a complete crisp graph.

It is clear from the following example.

Example 45.

We show that the underlying crisp graph of a complete fuzzy graph is complete crisp graph. Let V=u,v,w,x. Define the fuzzy set σ on V as σu=0.7,σv=0.8,σw=1, and σx=0.6. Define a fuzzy set μ on E such that μu,v=μu,w=0.7,μu,x=μv,x=μw,x=0.6, and μv,w=0.8. Then μu,v=σuσ(v) for all u,vV. Thus, G=(V,σ,μ) is a complete fuzzy graph. Now,G=V,E, where V=uVσu>0=u,v,w,x and E=u,v,u,vVμu,v>0={u,v,u,w,u,x,v,x,w,x,v,w}. This shows that each vertex in G joins each of the other vertices in G exactly by one edge. Therefore, G is a complete crisp graph K4.

Remark 46.

For complete fuzzy graph G with n vertices, by Lemma 44, G=Kn, where Kn is a complete crisp graph.

The following is an important theorem for computing the fuzzy chromatic polynomial of a complete fuzzy graph explicitly.

Theorem 47.

Let G be a complete fuzzy graph with n vertices. If αI=L{0} and β=min(L) then (10)PαfG,k=PKn,kifα=0andβPGα,kifβ<α1

Proof.

Let G=(V,σ,μ) be a complete fuzzy graph with n vertices and let G=(V,E) be the underlying crisp graph of G, where V=uVσ(u)>0 and E=u,v,u,vVμ(u,v)>0. For αI=L{0}, where L is a level set of G now Gα=(Vα,Eα) where Vα=uVσ(u)α and Eα=u,vμ(u,v)α. Let us prove the theorem by considering three cases.

Case 1. Let α=0, and since G is a fuzzy graph with n vertices then, by Theorem 39, we have PαfG,k=PKn,k.

Case 2. Let α=β, where β=minL; since G is a fuzzy graph then, by Theorem 40, we have(11)PαfG,k=PG,kand since G is complete fuzzy graph, then, by Remark 46 and (11), we get PαfG,k=PKn,k.

Case 3. Let β<α1; since α is in I and G is a fuzzy graph then by Theorem 41PαfG,k=PGα,k. This completes the proof.

Remark 48.

For a complete fuzzy graph G=(V,σ,μ) with crisp vertices (i.e., σv=1,vV) and V=n, PαfG,k=PKn,k for all αI.

The fuzzy chromatic polynomial of a complete fuzzy graph with L=V can be defined in terms of the chromatic polynomial of complete crisp graph as follows.

Definition 49.

Let G=(V,σ,μ) be a complete fuzzy graph with V=n vertices. Let L be a level set of G and αI=LU{0}. Define β0=min(L)=β, β1=min(L\β0), β2=min(L\β0,β1), …,βn-1=min(L\β0,β1,,βn-2).

If L=V=n,  n1, then the fuzzy chromatic polynomial of a complete fuzzy graph G is defined as (12)PαfG,k=PKn,kifα=0&β0PKn-1,kifα=β1PKn-2,kifα=β2PK1,kifα=βn-1where Kn, n1 is a complete crisp graph with n vertices.

Note that the advantage of Definition 49 is that the fuzzy chromatic polynomial of a complete fuzzy graph with V=L can be obtained without using the formal procedures. This situation can be illustrated in the following example.

Example 50.

Consider the complete fuzzy graph G in Figure 7.

A complete fuzzy graph G with V=L.

In Figure 7, V=4, I=L0, where L={0.6,0.7,0.8,1}. Therefore, L=4. Also, β0=0.6,β1=0.7, β2=0.8, and β3=1.

Since L=V=4, then, by Definition 49, the fuzzy chromatic polynomial of the complete fuzzy graph G is(13)PαfG,k=kk-1k-2k-3,α=0&0.6kk-1k-2,α=0.7kk-1,α=0.8k,α=1A fuzzy graph is a cycle and a fuzzy cycle as defined in [9, 32].

Definition 51 (see [<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B32">32</xref>]).

Let G=(V,σ,μ) be a fuzzy graph. Then

G is called a cycle if G=(V,E) is a cycle;

G is called a fuzzy cycle if G=(V,E) is a cycle and unique (x,y)E such that μ(x,y)={μ(u,v)(u,v)E}.

Remark 52.

If a fuzzy graph G is cycle with n vertices, then G=Cn, where Cn is a cycle crisp graph with n vertices.

The following theorem is important for computing the fuzzy chromatic polynomial of a fuzzy graph which is cycle.

Theorem 53.

Let G be a fuzzy graph with n vertices. Then G is a cycle if and only if(14)PαfG,k=PKn,kifα=0PCn,kifα=βPGα,kifβ<α1where Kn and Cn are a complete crisp graph and cycle crisp graph with n vertices, respectively.

Proof.

Let G=(V,σ,μ) be a fuzzy graph with n vertices. Suppose G be a cycle. Since G is a cycle, G is a cycle crisp graph. For αI=L{0}, where L is a level set of G, now Gα=(Vα,Eα), where Vα=uVσ(u)α and Eα=u,vμ(u,v)α. Let us prove the theorem by considering three cases.

Case 1. Let α=0, and since G is a fuzzy graph with n vertices then by Theorem 39, we have PαfG,k=PKn,k.

Case 2. Let α=β, where β=minL; since G is a fuzzy graph then, by Theorem 40, we have (15)PαfG,k=PG,kand since G is cycle with n vertices, then, by Remark 52 and (15), we get PαfG,k=PCn,k.

Case 3. Let β<α1; since α is in I and G is a fuzzy graph then, by Theorem 41, PαfG,k=PGα,k. Hence, the result holds.

Conversely, we shall prove it by contrapositive. Suppose G is not a cycle. Then, by Definition 51, G is not a cycle. This implies that GCn. Hence,(16)PαfG,kPKn,kifα=0PCn,kifα=βPGα,kifβ<α1Therefore, if (14) is true, then G is a cycle. This completes the proof.

Corollary 54.

Let G be a fuzzy graph. If G is a fuzzy cycle with n vertices; then (14) is true.

Proof.

Let G be a fuzzy cycle with n vertices. Since G is fuzzy cycle, then its G is cycle. Then by Definition 51, G is a cycle. Then by Theorem 53, the result is immediate.

Remark 55.

The converse of Corollary 54 is not true. It can be seen from the following example.

Example 56.

Consider the fuzzy graph G given in Figure 8.

A fuzzy graph G.

In Figure 8, clearly G is a cycle. So, G is cycle. For αI=L0,where L={0.2,0.6,0.8,1}, since G is cycle, then, by (14), the fuzzy chromatic polynomial of G is (17)PαfG,k=PK4,kifα=0PC4,kifα=0.2PGα,kif0.2<α1But G is not fuzzy cycle.

Remark 57.

In general, the meaning of fuzzy chromatic polynomial of a fuzzy graph depends on the sense of the index α. It can be interpreted that for lower values of α more numbers of different k-colorings are obtained. On the other hand, for higher values of α less number of different k-colorings are obtained. Therefore, the fuzzy chromatic polynomial sums up all this information so as to manage the fuzzy problem.

5. Conclusions

In this paper, the concept of fuzzy chromatic polynomial of fuzzy graph with crisp and fuzzy vertex sets is introduced. The fuzzy chromatic polynomial of fuzzy graph is defined based on α-cuts of the fuzzy graph. Some properties of the fuzzy chromatic polynomial of fuzzy graphs are investigated. The degree of fuzzy chromatic polynomial of a fuzzy graph is less than or equal to the number of vertices in fuzzy graph which is a significant difference from the crisp graph theory. Further, the fuzzy chromatic polynomial of fuzzy graph with fuzzy vertex set will decrease when the value of α will increase. Also, more results on fuzzy chromatic polynomial of fuzzy graphs have been proved. The relation between the fuzzy chromatic polynomial of fuzzy graph and its underlying crisp graph and the relation between the fuzzy chromatic polynomial of fuzzy graph and complete crisp graph are established. Finally, the fuzzy chromatic polynomial for complete fuzzy graph and fuzzy cycle is studied. Also, the fuzzy chromatic polynomial of complete fuzzy graph is defined explicitly when V=L. We are working on fuzzy chromatic polynomial based on different types of fuzzy coloring functions as an extension of this study.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

Both authors contributed equally and significantly in writing this article. Both authors read and approved the final manuscript.

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