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. 2016 Aug 30;5(1):1448. doi: 10.1186/s40064-016-3066-8

On m-polar fuzzy graph structures

Muhammad Akram 1, Rabia Akmal 1, Noura Alshehri 2,
PMCID: PMC5005259  PMID: 27652024

Abstract

Sometimes information in a network model is based on multi-agent, multi-attribute, multi-object, multi-polar information or uncertainty rather than a single bit. An m-polar fuzzy model is useful for such network models which gives more and more precision, flexibility, and comparability to the system as compared to the classical, fuzzy and bipolar fuzzy models. In this research article, we introduce the notion of m-polar fuzzy graph structure and present various operations, including Cartesian product, strong product, cross product, lexicographic product, composition, union and join of m-polar fuzzy graph structures. We illustrate these operations by several examples. We also investigate some of their related properties.

Keywords: m-Polar fuzzy graph structure (m-PFGSs), Composition, Cartesian product, Strong product, Cross product, Lexicographic product, Join, Union of two m-PFGSs

Background

Graph theory have applications in many areas of computer science including data mining, image segmentation, clustering, image capturing, networking. A graph structure, introduced by Sampathkumar (2006), is a generalization of undirected graph which is quite useful in studying some structures including graphs, signed graphs, graphs in which every edge is labeled or colored. A graph structure helps to study the various relations and the corresponding edges simultaneously.

A fuzzy set (Zadeh 1965) is an important mathematical structure to represent a collection of objects whose boundary is vague. Fuzzy models are becoming useful because of their aim in reducing the differences between the traditional models used in engineering and science. Nowadays fuzzy sets are playing a substantial role in chemistry, economics, computer science, engineering, medicine and decision making problems. In 1998, Zhang (1998) generalized the idea of a fuzzy set and gave the concept of bipolar fuzzy set on a given set X as a map which associates each element of X to a real number in the interval [-1,1]. In 2014, Chen et al. (2014) introduced the idea of m-polar fuzzy sets as an extension of bipolar fuzzy sets and showed that bipolar fuzzy sets and 2-polar fuzzy sets are cryptomorphic mathematical notions and that we can obtain concisely one from the corresponding one in Chen et al. (2014). The idea behind this is that “multipolar information” (not just bipolar information which corresponds to two-valued logic) exists because data for a real world problem are sometimes from n agents (n2). For example, the exact degree of telecommunication safety of mankind is a point in [0,1]n(n7×109) because different person has been monitored different times. There are many examples such as truth degrees of a logic formula which are based on n logic implication operators (n2), similarity degrees of two logic formula which are based on n logic implication operators (n2), ordering results of a magazine, ordering results of a university and inclusion degrees (accuracy measures, rough measures, approximation qualities, fuzziness measures, and decision preformation evaluations) of a rough set.

Kauffman (1973) gave the definition of a fuzzy graph in 1973 on the basis of Zadeh’s fuzzy relations (Zadeh 1971). Rosenfeld (1975) discussed the idea of fuzzy graph in 1975. Further remarks on fuzzy graphs were given by Bhattacharya (1987). Several concepts on fuzzy graphs were introduced by Mordeson and Nair (2001). Akram et al. has discussed and introduced bipolar fuzzy graphs, regular bipolar fuzzy graphs, properties of bipolar fuzzy hypergraphs, bipolar fuzzy graph structures and bipolar fuzzy competition graphs in Akram (2011, (2013), Akram and Dudek (2012), Akram et al. (2013), Akram and Akmal (2016) and Al-Shehrie and Akram (2015). In 2015, Akram and Younas studied certain types of irregular m-polar fuzzy graphs in Akram and Younas (2016). Akram and Adeel studied m-polar fuzzy line graphs in Akram and Adeel (2016). Akram and Waseem introduced certain metrics in m-polar fuzzy graphs in Akram and Waseem (2016). Dinesh (2014) introduced the notion of a fuzzy graph structure and discussed some related properties. Akram and Akmal (2016) introduced the concept of bipolar fuzzy graph structures. In this research article, we introduce the notion of m-polar fuzzy graph structure and present various operations, including Cartesian product, strong product, cross product, lexicographic product, composition, union and join of m-polar fuzzy graph structures. We illustrate these operations by several examples. We also investigate some of their related properties. We have used standard definitions and terminologies in this paper. For other notations, terminologies and applications not mentioned in the paper, the readers are referred to Dinesh and Ramakrishnan (2011), Lee (2000) and Zhang (1994).

Preliminaries

In this section, we review some basic concepts that are necessary for fully benefit of this paper.

In 1965,  Zadeh (1965) introduced the notion of a fuzzy set as follows.

Definition 1

(Zadeh 1965, 1971) A fuzzy setμ in a universe X is a mapping μ:X[0,1]. A fuzzy relation on X is a fuzzy set ν in X×X. Let μ be a fuzzy set in X and ν fuzzy relation on X. We call ν is a fuzzy relation on μ if ν(x,y)min{μ(x),μ(y)}x,yX.

Recently, Akram and Akmal (2016) applied the concept of bipolar fuzzy sets to graph structures.

Definition 2

(Akram and Akmal 2016) Gbˇ=(M,N1,N2,,Nn) is called a bipolar fuzzy graph structure(BFGS) of a graph structure (GS) G=(U,E1,E2,,En) if M=(μMP,μMN) is a bipolar fuzzy set onU and for each i=1,2,,n,Ni=(μNiP,μNiN) is a bipolar fuzzy set onEi such that

μNiP(xy)μMP(x)μMP(y),μNiN(xy)μMN(x)μMN(y)xyEiU×U.

Note that μNiP(xy)=0=μNiN(xy) for all xyU×U-Ei and 0<μNiP(xy)1, -1μNiN(xy)<0xyEi, where U and Ei(i=1,2,,n) are called underlying vertex set and underlying i-edge sets of Gbˇ, respectively.

Definition 3

(Akram and Akmal 2016) Let Gbˇ=(M,N1,N2,,Nn) be a BFGS of a GSG=(U,E1,E2,,En). Let ϕ be any permutation on the set {E1,E2,,En} and the corresponding permutation on {N1,N2,,Nn}, i.e., ϕ(Ni)=Nj if and only if ϕ(Ei)=Eji.

If xyNr for some r and

μNiϕP(xy)=μMP(x)μMP(y)-jiμϕNjP(xy),μNiϕN(xy)=μMN(x)μMN(y)-jiμϕNjN(xy),i=1,2,,n,

then xyBmϕ, while m is chosen such that μNmϕP(xy)μNiϕP(xy)andμNmϕN(xy)μNiϕN(xy)i.

And BFGS (M,N1ϕ,N2ϕ,,Nnϕ) denoted by Gˇbϕc, is called the ϕ-complement of BFGSGbˇ.

Chen et al. (2014) introduced the notion of m-polar fuzzy set as a generalization of a bipolar fuzzy set.

Definition 4

(Chen et al. 2014) An m-polar fuzzy set (or a [0,1]m-set) on X is exactly a mapping A:X[0,1]m.

Note that [0,1]m (mth-power of [0, 1]) is considered as a poset with the point-wise order , where m is an arbitrary ordinal number (we make an appointment that m={n|n<m} when m>0), is defined by xypi(x)pi(y) for each im ( x,y[0,1]m), and pi:[0,1]m[0,1] is the ith projection mapping (im). 0=(0,0,,0) is the smallest element in [0,1]m and 1=(1,1,,1) is the largest element in [0,1]m. Akram and Waseem (2016) defined m-polar fuzzy relation as follows.

Definition 5

(Akram and Waseem 2016) Let C be an m-polar fuzzy subset of a non-empty set V. An m-polar fuzzy relation on C is an m-polar fuzzy subset D of V×V defined by the mapping D:V×V[0,1]m such that for all x,yV,piD(xy)inf{piC(x),piC(y)},i=1,2,,m, where piC(x) denotes the ith degree of membership of the vertex x and piD(xy) denotes the ith degree of membership of the edge xy.

An m-polar fuzzy graph was introduced by Chen et al. (2014) and modified by Akram and Waseem (2016).

Definition 6

(Akram and Waseem 2016), Chen et al. (2014) An m-polar fuzzy graph is a pair G=(C,D), where C:V[0,1]m is an m-polar fuzzy set in V and D:V×V[0,1]m is an m-polar fuzzy relation on V such that

piD(xy)inf{piC(x),piC(y)}

for all x,yV.

We note that piD(xy)=0 for all xyV×V-E for all i=1,2,3,,m.C is called the m-polar fuzzy vertex set of G and D is called the m-polar fuzzy edge set of G,  respectively. An m-polar fuzzy relation D on V is called symmetric if piD(xy)=piD(yx) for all x,yV.

m-Polar fuzzy graph structures

We first define the concept of an m-polar fuzzy graph structure.

Definition 7

Let G=(U,E1,E2,,En) be a graph structure (GS). Let C be an m-polar fuzzy set on U and Di an m-polar fuzzy set on Ei such that

pjDi(xy)inf{pjC(x),pjC(y)}

for all x,yU, in,jm and pjDi(xy)=0 for xyU×U\Ei,j. Then G(m)=(C,D1,D2,,Dn) is called an m-polar fuzzy graph structure (m-PFGS) on G where C is the m-polar fuzzy vertex set of G(m) and Di is the m-polar fuzzyi-edge set of G(m).

We illustrate the concept of an m-polar fuzzy graph structure with an example.

Example 8

Consider a graph structure G=(U,E1,E2) such that U={a1,a2,a3,a4}, E1={a1a2} and E2={a3a2,a2a4}. Let C, D1 and D2 be 4-polar fuzzy sets on U,E1 and E2, respectively, defined by the following tables:

C a1 a2 a3 a4
p1C 0.1 0.3 0.4 0.2
p2C 0.0 0.6 0.0 0.0
p3C 0.0 0.2 0.4 0.3
p4C 0.1 0.0 0.4 0.4
Di (a1a2)1 (a3a2)2 (a2a4)2
p1Di 0.1 0.2 0.2
p2Di 0.0 0.0 0.0
p3Di 0.0 0.2 0.2
p4Di 0.0 0.0 0.0

By simple calculations, it is easy to check that G(m)=(C,D1,D2) is a 4-polar fuzzy graph structure of G as shown in Fig. 1. Note that we represent xyDi as (xy)i=(p1Di(xy),,pmDi(xy))i in all tables and the figures.

Fig. 1.

Fig. 1

4-Polar fuzzy graph structure

Note that operations on m-polar fuzzy sets are generalization of operations on bipolar fuzzy sets. We apply the concept of m-polar fuzzy sets on some operations of graph structures.

Definition 9

Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be two m-PFGSs. Then the Cartesian product of G(m)1 and G(m)2 is given by

G(m)1×G(m)2=(C1×C2,D11×D21,D12×D22,,D1n×D2n)

where the mappings C1×C2:U1×U2[0,1]m and D1i×D2i:E1i×E2i[0,1]m (for in) are respectively defined by

pj(C1×C2)(x1x2)=pjC1(x1)pjC2(x2),x1x2U1×U2

and

pj(D1i×D2i)((xx2)(xy2))=pjC1(x)pjD2i(x2y2),xU1,x2y2E2i,pj(D1i×D2i)((x1y)(y1y))=pjC2(y)pjD1i(x1y1),yU2,x1y1E1i,

where j varies from 1 to m.

We illustrate Cartesian product of G(m)1 and G(m)2 with an example.

Example 10

Let G(m)1=(C,D1,D2) be a 4-PFGS of graph structure G1=(U,E1,E2) where U={b1,b2,b3},E1={b1b2} and E2={b2b3}.G(m)1 is drawn and shown in the Fig. 2.

Fig. 2.

Fig. 2

4-Polar fuzzy graph structure

The Cartesian product of G(m) (Fig. 1) and G(m)1, given by G(m)×G(m)1=(C×C,D1×D1,D2×D2), is as shown in Fig. 3. In the figure, a Di×Di-edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.

Fig. 3.

Fig. 3

Cartesian product of two 4-PFGSs

We now formulate Cartesian product of G(m)1 and G(m)2 as a proposition.

Proposition 11

Cartesian product of twom-polar fuzzy graph structures is anm-polar fuzzy graph structure.

Proof

Let GS G=(U1×U2,E11×E21,E12×E22,,E1n×E2n) be the Cartesian product of GSs G1=(U1,E11,E12,,E1n) and G2=(U2,E21,E22,,E2n). Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be respective m-PFGSs of G1 and G2. Then (C1×C2,D11×D21,D12×D22,,D1n×D2n) is an m-PFGS of G.By the Definition 9 of Cartesian product, C1×C2 is an m-polar fuzzy set of U1×U2 and D1i×D2i is an m-polar fuzzy set of E1i×E2i for all i. So the remaining task is to prove that D1i×D2i is an m-polar fuzzy relation on C1×C2 for all i. For this, some cases are discussed, as follows:

Case 1. When xU1 and x2y2E2i

pj(D1i×D2i)((xx2)(xy2))=pjC1(x)pjD2i(x2y2)pjC1(x)[inf{pjC2(x2),pjC2(y2)}]=inf{pjC1(x)pjC2(x2),pjC1(x)pjC2(y2)}=inf{pj(C1×C2)(xx2),pj(C1×C2)(xy2)},jm.

Case 2. When yU2,x1y1E1i

pj(D1i×D2i)((x1y)(y1y))=pjC2(y)pjD1i(x1y1)pjC2(y)[inf{pjC1(x1),pjC1(y1)}]=inf{pjC2(y)pjC1(x1),pjC2(y)pjC1(y1)}=inf{pjC1(x1)pjC2(y),pjC1(y1)pjC2(y)}=inf{pj(C1×C2)(x1y),pj(C1×C2)(y1y)},jm.

Both cases hold for every in. This completes the proof.□

We define cross product of G(m)1 and G(m)2 by an example.

Definition 12

Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be two m-PFGSs. Then the cross product of G(m)1 and G(m)2 is given by

G(m)1G(m)2=(C1C2,D11D21,D12D22,,D1nD2n)

where the mappings C1C2:U1U2[0,1]m and D1iD2i:E1iE2i[0,1]m (for in) are respectively defined by

pj(C1C2)(x1x2)=pjC1(x1)pjC2(x2),x1x2U1U2=U1×U2

and

pj(D1iD2i)((x1x2)(y1y2))=pjD1i(x1y1)pjD2i(x2y2),x1y1E1i,x2y2E2i,

where j varies from 1 to m.

We explain the concept of cross product of two m-polar fuzzy graph structures with an example.

Example 13

Consider the 4-PFGSs G(m) and G(m)1 shown in the Figs. 1 and 2, respectively. The cross product of G(m) and G(m)1, given by G(m)G(m)1=(CC,D1D1,D2D2), is as shown in Fig. 4. In the figure, a DiDi-edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.

Fig. 4.

Fig. 4

Cross product of two 4-PFGSs

We formulate cross product of two m-polar fuzzy graph structures as a proposition.

Proposition 14

Cross product of two m-polar fuzzy graph structures is anm-polar fuzzy graph structure.

Proof

Let GS G=(U1U2,E11E21,E12E22,,E1nE2n) be the cross product of GSs G1=(U1,E11,E12,,E1n) and G2=(U2,E21,E22,,E2n). If G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) are respective m-PFGSs of G1 and G2 then (C1C2,D11D21,D12D22,,D1nD2n) is an m-PFGS of G. By the Definition 12 of cross product, C1C2 and D1iD2i are m-polar fuzzy sets of U1U2 and E1iE2i, respectively, for all i. So remaining task is to prove that D1iD2i is an m-polar fuzzy relation on C1C2 for all i. For this, proceed as follows:

If x1y1E1i and x2y2E2i, then

pj(D1iD2i)((x1x2)(y1y2))=pjD1i(x1y1)pjD2i(x2y2)[inf{pjC1(x1),pjC1(y1)}][inf{pjC2(x2),pjC2(y2)}]=inf{pjC1(x1)pjC2(x2),pjC1(y1)pjC2(y2)}=inf{pj(C1C2)(x1x2),pj(C1C2)(y1y2)},jm.

This holds for every in. Hence D1iD2i is an m-polar fuzzy relation on C1C2, for all i, which completes the proof.

We now define lexicographic product of m-polar fuzzy graph structures.

Definition 15

Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be two m-PFGSs. Then the lexicographic product of G(m)1 and G(m)2 is given by

G(m)1G(m)2=(C1C2,D11D21,D12D22,,D1nD2n)

where the mappings C1C2:U1U2[0,1]m and D1iD2i:E1iE2i[0,1]m (for in) are respectively defined by

pj(C1C2)(x1x2)=pjC1(x1)pjC2(x2),x1x2U1U2=U1×U2

and

pj(D1iD2i)((xx2)(xy2))=pjC1(x)pjD2i(x2y2),xU1,x2y2E2i,pj(D1iD2i)((x1x2)(y1y2))=pjD1i(x1y1)pjD2i(x2y2),x1y1E1i,x2y2E2i,

where j varies from 1 to m.

We explain the concept of lexicographic product of m-polar fuzzy graph structures by the following example.

Example 16

Consider the 4-PFGSs G(m) and G(m)1 shown in the Figs. 1 and 2, respectively. The lexicographic product of G(m) and G(m)1, given by G(m)G(m)1=(CC,D1D1,D2D2), is as shown in Fig. 5. In the figure, a DiDi-edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.

Fig. 5.

Fig. 5

Lexicographic product of two 4-PFGSs

We formulate Lexicographic product of two m-polar fuzzy graph structures as a proposition.

Proposition 17

Lexicographic product of two m-polar fuzzy graph structures is anm-polar fuzzy graph structure.

Proof

Let GS G=(U1U2,E11E21,E12E22,,E1nE2n) be the lexicographic product of GSs G1=(U1,E11,E12,,E1n) and G2=(U2,E21,E22,,E2n). If G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) are respective m-PFGSs of G1 and G2 then (C1C2,D11D21,D12D22,,D1nD2n) is an m-PFGS of G. By the Definition 15 of lexicographic product, C1C2 and D1iD2i are m-polar fuzzy sets of U1U2 and E1iE2i, respectively, for all i. Now, remaining task is to prove that D1iD2i is an m-polar fuzzy relation on C1C2 for all i. For this, we discuss two cases as follows:

Case 1. When xU1 and x2y2E2i

pj(D1iD2i)((xx2)(xy2))=pjC1(x)pjD2i(x2y2)pjC1(x)[inf{pjC2(x2),pjC2(y2)}]=inf{pjC1(x)pjC2(x2),pjC1(x)pjC2(y2)}=inf{pj(C1C2)(xx2),pj(C1C2)(xy2)},jm.

Case 2. When x1y1E1i and x2y2E2i,

pj(D1iD2i)((x1x2)(y1y2))=pjD1i(x1y1)pjD2i(x2y2)[inf{pjC1(x1),pjC1(y1)}][inf{pjC2(x2),pjC2(y2)}]=inf{pjC1(x1)pjC2(x2),pjC1(y1)pjC2(y2)}=inf{pj(C1C2)(x1x2),pj(C1C2)(y1y2)},jm.

This holds for every in. Hence D1iD2i is an m-polar fuzzy relation on C1C2, for all i, which completes the proof.

We now give definition of strong product of m-polar fuzzy graph structures.

Definition 18

Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be two m-PFGSs. Then the strong product of G(m)1 and G(m)2 is given by

G(m)1G(m)2=(C1C2,D11D21,D12D22,,D1nD2n)

where the mappings C1C2:U1U2[0,1]m and D1iD2i:E1iE2i[0,1]m (for in) are respectively defined by

pj(C1C2)(x1x2)=pjC1(x1)pjC2(x2),x1x2U1U2=U1×U2

and

pj(D1iD2i)((xx2)(xy2))=pjC1(x)pjD2i(x2y2),xU1,x2y2E2i,pj(D1iD2i)((x1y)(y1y))=pjC2(y)pjD1i(x1y1),yU2,x1y1E1i,pj(D1iD2i)((x1x2)(y1y2))=pjD1i(x1y1)pjD2i(x2y2),x1y1E1i,x2y2E2i,

where j varies from 1 to m.

We illustrate the idea of strong product of m-polar fuzzy graph structures by the following example.

Example 19

Consider the 4-PFGSs G(m) and G(m)1 shown in the Figs. 1 and 2, respectively. The strong product of G(m) and G(m)1, given by G(m)G(m)1=(CC,D1D1,D2D2), is as shown in Fig. 6. In the figure, a DiDi-edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.

Fig. 6.

Fig. 6

Strong product of two 4-PFGSs

We formulate strong product of G(m)1 and G(m)2 as a proposition.

Proposition 20

Strong product of two m-polar fuzzy graph structures is anm-polar fuzzy graph structure.

Proof

Let GS G=(U1U2,E11E21,E12E22,,E1nE2n) be the strong product of GSs G1=(U1,E11,E12,,E1n) and G2=(U2,E21,E22,,E2n). Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be respective m-PFGSs of G1 and G2. Then (C1C2,D11D21,D12D22,,D1nD2n) is an m-PFGS of G. By Definition 18 of strong product, C1C2 is an m-polar fuzzy set of U1U2 and D1iD2i is an m-polar fuzzy set of E1iE2i for all i. So the remaining task is to prove that D1iD2i is an m-polar fuzzy relation on C1C2 for all i. For this, some cases are discussed, as follows:

Case 1. When xU1 and x2y2E2i

pj(D1iD2i)((xx2)(xy2))=pjC1(x)pjD2i(x2y2)pjC1(x)[inf{pjC2(x2),pjC2(y2)}]=inf{pjC1(x)pjC2(x2),pjC1(x)pjC2(y2)}=inf{pj(C1C2)(xx2),pj(C1C2)(xy2)},jm.

Case 2. When yU2,x1y1E1i

pj(D1iD2i)((x1y)(y1y))=pjC2(y)pjD1i(x1y1)pjC2(y)[inf{pjC1(x1),pjC1(y1)}]=inf{pjC2(y)pjC1(x1),pjC2(y)pjC1(y1)}=inf{pjC1(x1)pjC2(y),pjC1(y1)pjC2(y)}=inf{pj(C1C2)(x1y),pj(C1C2)(y1y)},jm.

Case 3. When x1y1E1i and x2y2E2i,

pj(D1iD2i)((x1x2)(y1y2))=pjD1i(x1y1)pjD2i(x2y2)[inf{pjC1(x1),pjC1(y1)}][inf{pjC2(x2),pjC2(y2)}]=inf{pjC1(x1)pjC2(x2),pjC1(y1)pjC2(y2)}=inf{pj(C1C2)(x1x2),pj(C1C2)(y1y2)},jm.

All three cases hold for every in. This completes the proof.

We define the notion of composition of two m-polar fuzzy graph structures.

Definition 21

Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be two m-PFGSs. Then composition of G(m)1 and G(m)2 is given by

G(m)1G(m)2=(C1C2,D11D21,D12D22,,D1nD2n)

where the mappings C1C2:U1U2[0,1]m and D1iD2i:E1iE2i[0,1]m (for in) are respectively defined by

pj(C1C2)(x1x2)=pjC1(x1)pjC2(x2),x1x2U1U2=U1×U2

and

pj(D1iD2i)((xx2)(xy2))=pjC1(x)pjD2i(x2y2),xU1,x2y2E2i,pj(D1iD2i)((x1y)(y1y))=pjC2(y)pjD1i(x1y1),yU2,x1y1E1i,pj(D1iD2i)((x1x2)(y1y2))=pjD1i(x1y1)pjC2(x2)pjC2(y2),x1y1E1i,x2,y2U2,such thatx2y2,

where j varies from 1 to m.

We discuss the notion of composition of two m-polar fuzzy graph structures by the following example.

Example 22

Consider the 4-PFGSs G(m) and G(m)1 shown in the Fig. 1 and The composition of G(m) and G(m)1, given by G(m)G(m)1=(CC,D1D1,D2D2), is as shown in Fig. 7. In the figure, a DiDi-edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.

Fig. 7.

Fig. 7

Composition of two 4-PFGSs

We present composition of two m-polar fuzzy graph structures as a propostion.

Proposition 23

Composition of two m-polar fuzzy graph structures is anm-polar fuzzy graph structure.

Proof

Let GS G=(U1U2,E11E21,E12E22,,E1nE2n) be the composition of GSs G1=(U1,E11,E12,,E1n) and G2=(U2,E21,E22,,E2n). Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be respective m-PFGSs of G1 and G2. Then (C1C2,D11D21,D12D22,,D1nD2n) is an m-PFGS of G. By Definition 21 of composition, C1C2 is an m-polar fuzzy set of U1U2 and D1iD2i is an m-polar fuzzy set of E1iE2i for all i. Therefore the remaining task is to show that D1iD2i is an m-polar fuzzy relation on C1C2 for all i. For this, consider the following cases:

Case 1. When xU1 and x2y2E2i

pj(D1iD2i)((xx2)(xy2))=pjC1(x)pjD2i(x2y2)pjC1(x)[inf{pjC2(x2),pjC2(y2)}]=inf{pjC1(x)pjC2(x2),pjC1(x)pjC2(y2)}=inf{pj(C1C2)(xx2),pj(C1C2)(xy2)},jm.

Case 2. When yU2,x1y1E1i

pj(D1iD2i)((x1y)(y1y))=pjC2(y)pjD1i(x1y1)pjC2(y)[inf{pjC1(x1),pjC1(y1)}]=inf{pjC2(y)pjC1(x1),pjC2(y)pjC1(y1)}=inf{pjC1(x1)pjC2(y),pjC1(y1)pjC2(y)}=inf{pj(C1C2)(x1y),pj(C1C2)(y1y)},jm.

Case 3. When x1y1E1i and x2,y2U2, such that x2y2,

pj(D1iD2i)((x1x2)(y1y2))=pjD1i(x1y1)pjC2(x2)pjC2(y2)[inf{pjC1(x1),pjC1(y1)}]pjC2(x2)pjC2(y2)=inf{[pjC1(x1)pjC2(x2)pjC2(y2)],[pjC1(y1)pjC2(x2)pjC2(y2)]}inf{[pjC1(x1)pjC2(x2)],[pjC1(y1)pjC2(y2)]}=inf{pj(C1C2)(x1x2),pj(C1C2)(y1y2)},jm.

All three cases hold for every in. This completes the proof.□

We now introduce the concept of union of two m-polar fuzzy graph structures.

Definition 24

Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be two m-PFGSs. Then union of G(m)1 and G(m)2 is given by

G(m)1G(m)2=(C1C2,D11D21,D12D22,,D1nD2n)

where the mappings C1C2:U1U2[0,1]m and D1iD2i:E1iE2i[0,1]m (for in) are respectively defined by

pj(C1C2)(x)=pjC1(x),xU1\U2pjC2(x),xU2\U1pjC1(x)pjC2(x),xU1U2

and

pj(D1iD2i)(x1x2)=pjD1i(x1x2),x1x2E1i\E2ipjD2i(x1x2),x1x2E2i\E1ipjD1i(x1x2)pjD2i(x1x2),x1x2E1iE2i

where j varies from 1 to m.

We describe the concept of union of two m-polar fuzzy graph structures with an example.

Example 25

Consider the 4-PFGSs G(m) and G(m)1 shown in the Figs. 1 and 2, respectively. The union of G(m) and G(m)1, given by G(m)G(m)1=(CC,D1D1,D2D2), is as shown in Fig. 8. In the figure, a DiDi-edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.

Fig. 8.

Fig. 8

Union of two m-PFGSs

Proposition 26

Union of two m-polar fuzzy graph structures is anm-polar fuzzy graph structure.

Proof

Let GS G=(U1U2,E11E21,E12E22,,E1nE2n) be the union of GSs G1=(U1,E11,E12,,E1n) and G2=(U2,E21,E22,,E2n). Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be respective m-PFGSs of G1 and G2. Then (C1C2,D11D21,D12D22,,D1nD2n) is an m-PFGS of G. From the Definition 24 of union, C1C2 is an m-polar fuzzy set of U1U2 and D1iD2i is an m-polar fuzzy set of E1iE2i for all i. So the remaining task is to show that D1iD2i is an m-polar fuzzy relation on C1C2 for all i. For this, consider following cases:

Case 1. When x1x2E1i\E2i, then there are three possibilities (i) x1,x2U1 (ii) x1U1,x2U1U2 (ii) x2U1,x1U1U2. So for all jm

pj(D1iD2i)(x1x2)=pjD1i(x1x2)inf{pjC1(x1),pjC1(x2)}=inf{pj(C1C2)(x1),pj(C1C2)(x2)},ifx1,x2U1.inf[pjC1(x1),max{pjC1(x2),pjC2(x2)}]=inf{pj(C1C2)(x1),pj(C1C2)(x2)},ifx1U1,x2U1U2.inf[max{pjC1(x1),pjC2(x1)},pjC1(x2)]=inf{pj(C1C2)(x1),pj(C1C2)(x2)},ifx2U1,x1U1U2.

Case 2. When x1x2E2i\E1i, then there are three possibilities (i) x1,x2U2 (ii) x1U2,x2U1U2 (ii) x2U2,x1U1U2. So for all jm

pj(D1iD2i)(x1x2)=pjD2i(x1x2)inf{pjC2(x1),pjC2(x2)}=inf{pj(C1C2)(x1),pj(C1C2)(x2)},ifx1,x2U2.inf[pjC2(x1),max{pjC1(x2),pjC2(x2)}]=inf{pj(C1C2)(x1),pj(C1C2)(x2)},ifx1U2,x2U1U2.inf[max{pjC1(x1),pjC2(x1)},pjC2(x2)]=inf{pj(C1C2)(x1),pj(C1C2)(x2)},ifx2U2,x1U1U2.

Case 3. When x1x2E2iE1i, then x1,x2U1U2. So

pj(D1iD2i)(x1x2)=[pjD1i(x1x2)][pjD2i(x1x2)][inf{pjC1(x1),pjC1(x2)}][inf{pjC2(x1),pjC2(x2)}]=inf[inf{pjC1(x1),pjC1(x2)}{pjC2(x1)},inf{pjC1(x1),pjC1(x2)}{pjC2(x2)}]inf[{pjC1(x1)}{pjC2(x1)},{pjC1(x2)}{pjC2(x2)}]=inf[pj(C1C2)(x1),pj(C1C2)(x2)],jm.

All three cases hold for every in. Hence D1iD2i is an m-polar fuzzy relation on C1C2 for all i. This completes the proof.

Theorem 27

If GSG=(U1U2,E11E21,E12E22,,E1nE2n) is the union of GSsG1=(U1,E11,E12,,E1n) andG2=(U2,E21,E22,,E2n). Then everym-PFGS (C,D1,D2,,Dn) ofGis the union of an m-PFGSG(m)1 ofG1and an m-PFGSG(m)2 ofG2.

Proof

Observe that C=C1C2, Di=D1iD2i and C1,C2,D1i and D2i are m-polar fuzzy sets on U1,U2,E1i and E2i, respectively, for in if for every j, we define C1,C2,D1i and D2i as:

pjC1(x)=pjC(x),ifuU1\U2.pjC2(x)=pjC(x),ifuU2\U1.pjC1(x)=pjC2(x)=pjC(x),ifuU2U1.pjD1i(x1x2)=pjDi(x1x2),if(x1x2)E1i\E2i.pjD2i(x1x2)=pjDi(x1x2),if(x1x2)E2i\E1i.pjD1i(x1x2)=pjD2i(x1x2)=pjDi(x1x2),if(x1x2)E1iE2i.

For k=1,2, Dki is an m-polar fuzzy relation on Ck, since

pjDki(x1x2)=pjDi(x1x2)inf{pjC(x1),pjC(x2)}=inf{pjCk(x1),pjCk(x2)}.

Therefore, G(m)k=(Ck,Dk1,,Dkn) is a m-PFGS of Gk for k=1,2 and m-PFGS (C,D1,,Dn) is union of m-PFGS G(m)1=(C1,D11,D12,,D1n) and m-PFGS G(m)2=(C2,D21,D22,,D2n). Hence every m-PFGS of G=kGk, is the union of some m-PFGSs of Gk for k=1,2.

Finally, we study the concept of join of two m-polar fuzzy graph structures.

Definition 28

Let G(m)1=(C1,D11,D12,,D1n) and G(m)2=(C2,D21,D22,,D2n) be two m-PFGSs such that U1U2=. Let U1i={xU1:AlltheedgesincidentwithxareE1i-edges} and U2i={xU2:AlltheedgesincidentwithxareE2i-edges}. Then join of G(m)1 and G(m)2 is given by

G(m)1+G(m)2=(C1+C2,D11+D21,D12+D22,,D1n+D2n)

where the mappings C1+C2:U1+U2[0,1]m and D1i+D2i:E1i+E2i[0,1]m (for in) are respectively defined by

pj(C1+C2)(x)=pjC1(x),xU1pjC2(x),xU2

and

pj(D1i+D2i)(x1x2)=pjD1i(x1x2),x1x2E1ipjD2i(x1x2),x1x2E2iinf{pjC1(x1),pjC2(x2)},x1U1i,x2U2i

where j varies from 1 to m.

Example 29

Consider the 4-PFGSs G(m) and G(m)1 shown in the Figs. 1 and 2, respectively. The join of G(m) and G(m)1, given by G(m)+G(m)1=(C+C,D1+D1,D2+D2), is as shown in Fig. 9. In the figure, a Di+Di-edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.

Fig. 9.

Fig. 9

Join of two m-PFGSs

Proposition 30

Let GSG=(U1+U2,E11+E21,E12+E22,,E1n+E2n) be the join of GSsG1=(U1,E11,E12,,E1n) andG2=(U2,E21,E22,,E2n). LetG(m)1=(C1,D11,D12,,D1n) andG(m)2=(C2,D21,D22,,D2n)be respective m-PFGSs of G1 andG2. Then(C1+C2,D11+D21,D12+D22,,D1n+D2n)is an m-PFGS of G.

Proof

From the Definition 28 of Join, C1+C2 is an m-polar fuzzy set of U1+U2 and D1i+D2i is an m-polar fuzzy set of E1i+E2i for all i. So the remaining task is to show that D1i+D2i is an m-polar fuzzy relation on C1+C2 for all i. For this, consider following cases:

Case 1. When x1x2E1i, then x1,x2U1. So

pj(D1i+D2i)(x1x2)=pjD1i(x1x2)inf{pjC1(x1),pjC1(x2)}=inf{pj(C1+C2)(x1),pj(C1+C2)(x2)},jm.

Case 2. When x1x2E2i, then x1,x2U2. So

pj(D1i+D2i)(x1x2)=pjD2i(x1x2)inf{pjC2(x1),pjC2(x2)}=inf{pj(C1+C2)(x1),pj(C1+C2)(x2)},jm.

Case 3. When x1U1i,x2U2i, then x1U1,x2U2. So

pj(D1i+D2i)(x1x2)=[pjC1(x1)][pjC2(x2)]=[pj(C1+C2)(x1)][pj(C1+C2)(x2)]=inf[pj(C1+C2)(x1),pj(C1+C2)(x2)],jm.

Hence D1i+D2i is an m-polar fuzzy relation on C1+C2 in all three cases. All cases hold for every in. This completes the proof.

Theorem 31

If GSG=(U1+U2,E11+E21,E12+E22,,E1n+E2n) is the join of GSsG1=(U1,E11,E12,,E1n) andG2=(U2,E21,E22,,E2n). Then every strongm-PFGS (C,D1,D2,,Dn) ofGis the join of a strong m-PFGS ofG1 and a strongm-PFGS of G2.

Proof

Let (C,D1,D2,,Dn) be a strong m-PFGS of G. Define C1,C2,D1i and D2i for every j,  as follows:

pjC1(x)=pjC(x),ifuU1,pjC2(x)=pjC(x),ifuU2,pjD1i(x1x2)=pjDi(x1x2),if(x1x2)E1i,pjD2i(x1x2)=pjDi(x1x2),if(x1x2)E2i.

Observe that C1,C2,D1i and D2i are m-polar fuzzy sets on U1,U2,E1i and E2i, respectively, for in. For k=1,2, Dki is an m-polar fuzzy relation on Ck, so G(m)k=(Ck,Dk1,,Dkn) is a strong m-PFGS of Gk, since

pjDki(x1x2)=pjDi(x1x2)=inf{pjC(x1),pjC(x2)}=inf{pjCk(x1),pjCk(x2)}

for all x1x2Eki. Moreover, C=C1+C2 and Di=D1i+D2i, since pjDi(x1x2)=pj(D1i+D2i)(x1x2) for all x1x2E1iE2i and pjDi(x1x2)=inf{pjC(x1),pjC(x2)}=inf{pjC1(x1),pjC2)(x2)}=pj(D1i+D2i)(x1x2) for all x1U1i,x2U2i. Therefore m-PFGS (C,D1,,Dn) is join of m-PFGS G(m)1=(C1,D11,D12,,D1n) and m-PFGS G(m)2=(C2,D21,D22,,D2n). Hence a strong m-PFGS of G=G1+G2 is the join of a strong m-PFGSs of G1 and a strong m-PFGSs of G2. Which completes the proof.

Conclusions

A graph structure is a useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. It helps to study various relations and the corresponding edges simultaneously. We have introduced the notion of m-polar fuzzy graph structure, and presented various methods of their construction. We are extending our work to (1) domination in bipolar fuzzy graph structure, (2) bipolar fuzzy soft graph structures, (3) roughness in graph structures, (4) intuitionistic fuzzy soft graph structures, and (5) multiple-attribute decision making methods based on m-polar fuzzy graph structures.

Authors' contributions

The authors have introduced the notion of m-polar fuzzy graph structure, and presented various methods of their construction. All authors read and approved the final manuscript.

Acknowlegements

The authors are thankful to the referees for their valuable comments and suggestions.

Competing interests

The authors declare that they have no competing interests.

Contributor Information

Muhammad Akram, Email: m.akram@pucit.edu.pk.

Rabia Akmal, Email: rabia.akmal@ymail.com.

Noura Alshehri, Email: nalshehrie@kau.edu.sa.

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