Ghorai and Pal SpringerPlus (2016)5:2104 DOI 10.1186/s40064-016-3783-z

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Some isomorphic properties of m-polar fuzzy graphs with applications

Ganesh Ghorai* and Madhumangal Pal

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*Correspondence: math. ganesh@mail.vidyasagar.ac.in Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore 721 102, India

Abstract

The theory of graphs are very useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. In this paper, we present a frame work to handle m-polar fuzzy information by combining the theory of m-polar fuzzy sets with graphs. We introduce the notion of weak self complement m-polar fuzzy graphs and establish a necessary condition for m-polar fuzzy graph to be weak self complement. Some properties of self complement and weak self complement m-polar fuzzy graphs are discussed. The order, size, busy vertices and free vertices of an m-polar fuzzy graphs are also defined and proved that isomorphic m-polar fuzzy graphs have same order, size and degree. Also, we have presented some results of busy vertices in isomorphic and weak isomorphic m-polar fuzzy graphs. Finally, a relative study of complement and operations on m-polar fuzzy graphs have been made. Applications of m-polar fuzzy graph are also given at the end.

Keywords: m-Polar fuzzy graphs, Order and size, Busy and free vertices, Isomorphisms, Self complement and weak self complement, 5-Polar fuzzy evaluation graph

Background

After the introduction of fuzzy sets by Zadeh (1965), fuzzy set theory have been included in many research fields. Since then, the theory of fuzzy sets has become a vigorous area of research in different disciplines including medical and life sciences, management sciences, social sciences engineering, statistic, graph theory, artificial intelligence, signal processing, multi agent systems, decision making and automata theory. In a fuzzy set, each element is associated with a membership value selected from the interval [0, 1]. Zhang (1994, 1998) introduced the concept of bipolar fuzzy sets. Instead of using particular membership value as in fuzzy sets, m-polar fuzzy set can be used to represent uncertainty of a set more perfectly. Chen et al. (2014) introduced the notion of m-polar fuzzy set as a generalization of fuzzy set theory. The membership value in m-polar fuzzy set is more expressive in capturing uncertainty of data.

An m-polar fuzzy set on a non-void set X is a mapping ¡: X ^ [0,1]m. The idea behind this is that "multipolar information" exists because data of real world problems are sometimes come from multiple agents. m-polar fuzzy sets allow more graphical representation of vague data, which facilitates significantly better analysis in data relationships, incompleteness, and similarity measures. Graph theory besides being a well developed branch of Mathematics, it is an important tool for mathematical modeling.

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Realizing the importance, Rosenfeld (1975) introduced the concept of fuzzy graphs, Mordeson and Nair (2000) discussed about the properties of fuzzy graphs and hyper-graphs. After that, the operation of union, join, Cartesian product and composition on two fuzzy graphs was defined by Mordeson and Peng (1994). Sunitha and Vijayakumar (2002) further studied the other properties of fuzzy graphs. The concept of weak isomorphism, co-weak isomorphism and isomorphism between fuzzy graphs was introduced by Bhutani (1989). Later many researchers have worked on fuzzy graphs like in Bhutani et al. (2004); Al-Hawary (2011); Koczy (1992); Lee-kwang and Lee (1995); Nagoorgani and Radha (2008), Samanta and Pal (2011a, b, 2013, 2014, 2015). Akram (2011, 2013) introduced and defined different operations on bipolar fuzzy graphs. Again, Rashman-lou et al. (2015a, 2015b, 2016) studied bipolar fuzzy graphs with categorical properties, product of bipolar fuzzy graphs and their degrees, etc. Using these concepts many research is going on till date on bipolar fuzzy graphs such as Ghorai and Pal (2015b), Samanta and Pal (2012a, b, 2014), Yang et al. (2013). Chen et al. (2014) first introduced the concept of m-polar fuzzy graphs. Then Ghorai and Pal (2016a) presented properties of generalized m-polar fuzzy graphs, defined many operations and density of m-polar fuzzy graphs (2015a), introduced the concept of m-polar fuzzy planar graphs (2016b) and defined faces and dual of m-polar fuzzy planar graphs (2016c). Akram and You-nas (2015), Akram et al. (2016) introduced irregular m-polar fuzzy graphs and metrics in m-polar fuzzy graphs. In this paper, weak self complement m-polar fuzzy graphs is defined and a necessary condition is mentioned for an m-polar fuzzy graph to be weak self complement. Some properties of self complement and weak self complement m-polar fuzzy graphs are discussed. The order, size, busy vertices and free vertices of an m-polar fuzzy graphs are also defined and proved that isomorphic m-polar fuzzy graphs have same order, size and degree. Also, we have proved some results of busy vertices in isomorphic and weak isomorphic m-polar fuzzy graphs. Finally, a relative study of complement and operations on m-polar fuzzy graphs have been made.

Preliminaries

First of all we give the definitions of m-polar fuzzy sets, m-polar fuzzy graphs and other related definitions from the references (Al-Harary 1972; Lee 2000).

Throughout the paper, [0,1]m (m-power of [0, 1]) is considered to be a poset with point-wise order <, where m is a natural number. < is defined by x < y for each i = 1,2,..., m, pi(x) < pi(y) where x,y e [0,1]m and p : [0,1]m ^ [0,1] is the ¿th projection mapping.

As a generalization of bipolar fuzzy sets, Chen et al. (2014) defined the m-polar fuzzy sets in 2014.

Definition 1 (Chen et al. 2014) Let X be a non-void set. An m-polar fuzzy set on X is defined as a mapping ¡i: X ^ [0,1]m.

The m-polar fuzzy relation is defined below.

Definition 2 (Ghorai and Pal 2016a) Let A be an m-polar fuzzy set on a set X. An m-polar fuzzy relation on A is an m-polar fuzzy set B of X x X such that

Pi o B(x, y) < min{pi o A(x), pi o A (y)} for all x, y e X, i = 1,2,..., m. B is called symmetric if B(x, y) = B(y, x) for all x, y e X.

We define an equivalence relation ~ on V x V — {(x,x) : x e V} as follows: We say (x1,y1) ~ (x2,y2) if and only if either (x1,y1) = (x2,y2) or x1 = y2 and y1 = x2. Then we obtain an quotient set denoted by V2. The equivalence class containing the element (x, y) will be denoted as xy or yx.

We assume that G* = (V,E) is a crisp graph and G = (V,A, B) is an m-polar fuzzy graph of G* throughout this paper.

Chen et al. (2014) first introduced m-polar fuzzy graph. We have modified their definition and introduce generalized m-polar fuzzy graph as follows.

Definition 3 (Chen et al. 2014; Ghorai and Pal 2016a) An m-polar fuzzy graph (or generalized m-polar fuzzy graph) of G* = (V, E) is a pair G = (V, A, B) where A : V ^ [0,1]m is an m-polar fuzzy set in V and B : V2 ^ [0,1]m is an m-polar fuzzy set in V2 such that pi o B(xy) < min{pi o A(x),pi o A(y)} for all xy e V2, i = 1,2,..., m and B(xy) = 0 for all xy e V2 - E, (0 = (0,0,..., 0) is the smallest element in [0,1]m). We call A as the m-polar fuzzy vertex set of G and B as the m-polar fuzzy edge set of G.

Example 4 Let G* = (V,E) be a crisp graph where V = {u1, u2, u3, u4} and E = {u1u2, u2u3, u3u4, u4u1}. Then, G = (V, A, B) be a 3-polar fuzzy graph of G*

where A = ((0.5,0.7,0.8) (0.4,0.7,0.8) (0.7,0.6,0.8) (0.3,0.6,0.9) ] , ß = ((0.4,0.6,0.7) (0.3,0.6,0.5)

1 Ui , U2 , U3 , U4 J 1 U1U2 ' U2U3 '

(0.2,0.5,0.6) (0.2,0.4,0.8) (0,0,0) (0,0,0) \

U3 U4 ' U4 U\ ' U\ U3 ' U4 U2 J *

Ghorai and Pal (2016a) introduced many operations on m-polar fuzzy graphs such as Cartesian product, composition, union and join which are given below.

Definition 5 (Ghorai and Pal 2016a) The Cartesian product of two m-polar fuzzy graphs G1 = (V1, A1t B1) and G2 = (V2, A2, B2) of the graphs GJ and G| respectively is denoted as a pair G1 x G2 = (V1 x V2, A1 x A2, B1 x B2) such that for i = 1,2,..., m

(i) pi o (Ai x Ä2)(xi, X2) = min{pi o Ai (xi),pi o A2 (X2)} for all (X1, X2) e V1 x V2.

(ii) pi o (Bi x B2)((x,x2)(x,y2)) = min{pi o Ai(x),pi o B2(x2y2)} for all x e Vi,

x2y2 e £2.

(iii) Pi o (Bi x B2)((xi,z)(yi,z)) = min{pi o Bi(xiyi),pi o A2(z)} for all z e V2, xiyi e Ei. ________

(iv) pi o (Bi x B2)((x 1,x2)(yi,y2)) = 0 for all (X1,x2)(y1,y2) e (Vi x V2)2 - E.

Definition 6 (Ghorai and Pal 2016a) The composition of two m-polar fuzzy graphs G1 = (V1;A1,B{) and G2 = (V2,A2,B2) of the graphs G* = (Vi,Ei) and G* = (V2,E2) respectively is denoted as a pair G1[G2] = (V1 x V2,A1 o A2,B1 o B2) such that for i = 1,2,..., m

(i) pi o (Ai o A2)(xi, X2) = min{pi o Ai (xi),pi o A2 (X2)} for all (xi, X2) e Vi x V2.

(ii) Pi o (Bi o B2)((x,x2)(x,y2)) = min{pi o Ai(x),Pi o B2(x2y2)} for all x e Vi, X2y2 e E2.

(iii) Pi o (Bi o B2)((xi,z)(yi,z)) = min{pi o Bi(xiyi),pi o A2(z)} for all z e V2, xiyi e Ei.

(iv) Pi o (Bi o B2)((xi, X2)(yi, y2)) = min{pi o A2(X2), Pi o A2(y2), Pi o Bi(xiyi)} for all (xi,X2)(yi,y2) e E0 - E. ________

(v) Pi o (Bi O B2)((xi,X2)(yi,y2)) = 0 for all (xi,X2)(yi,y2) e (Vi x V2)2 - E0.

Definition 7 (Ghorai and Pal 2016a) The union Gi U G2 = (Vi U V2,Ai U A2,Bi U B2) of the m-polar fuzzy graphs G1 = (Vl, A1t B1) and G2 = (V2, A2, B2) of G] and G| respectively is defined as follows: for i = 1,2,..., m

(i) [pi ◦ Ai(x) ifx e Vi - V2

(i) pi o (Ai U A2)(x) = < pi o A2 (x) if x e V2 - Vi

I max{pi o Ai(x),pi o A2(x)} if x e Vi n V2.

(pi o Bi (xy) if xy e £1 - £2

Pi o B2 (xy) if xy e £2 - £1

max{pi o Bi(xy), pi o B2(xy)} if xy e £i n £2.

(iii) Pi O (Bi U B2)(xy) = 0 if xy e (Vi x V2)2 - Ei U E2.

Definition 8 (Ghorai and Pal 2016a) The join of the m-polar fuzzy graphs G1 = (Vl,A1tB1) and G2 = (V2,A2,B2) of G] and G| respectively is defined as a pair Gi + G2 = (Vi U V2, Ai + A2, Bi + B2) such that for i = 1,2,..., m

(i) pi o (Ai + A2)(x) = pi o (Ai u A2)(x) if x e Vi U V2.

(ii) pi o (Bi + B2)(xy) = pi o (Bi U B2)(xy) if xy e Ei U E2.

(iii) Pi o (Bi + B2)(xy) = min{Pi o Ai(x),Pi o A2(y)} if xy e E', where E' denotes the set of all edges joining the vertices of Vi and V2.

(iv) pi o (Bi + B2)(xy) = 0 if xy e (Vi x V2)2 - Ei U E2 UE'.

Remark 9 Later on, Akram et al. (2016) applied the concept of m-polar fuzzy sets on graph structure and also defined the above operations on them.

Different types of morphism are defined on m-polar fuzzy graphs by Ghorai and Pal (2016a).

Definition 10 (Ghorai and Pal 2016a) Let Gl = (Vl, Al, Bl) and G2 = (V2, A2, B2) be two m-polar fuzzy graphs of the graphs G:f = (Vl, El) and G| = (V2, E2) respectively. A homomorphism between G1 and G2 is a mapping <p : Vi ^ V2 such that for each i = 1,2,..., m

(i) pi o Ai (xi) < pi o A2 (<p (xi)) for all xi e Vi, _

(ii) pi o Bi(xiyi) < pi o B2(<p (xi)0 (yi)) for all xiyi e V2.

<f> is said to be an isomorphism if it is a bijective mapping and for i = 1,2,..., m

(i) Pi o Ai(xi) = Pi o Ä2($(xi)) for all xi e Vi, _

(ii) p,- o Bi(xiyi) = p,- o ß2(^(xi)^(yi)) for all xiyi e Vj2.

In this case, we write G1 = G2.

Definition 11 (Ghorai and Pal 2016a) A weak isomorphism between G1 = (V1, A1, B1 ) and G2 = (V2, A2, B2) is a bijective mapping < : V ^ V2 such that

(i) </> is a homomorphism,

(ii) pi o A1(x1) = pi o A2(^(x1)) for all x1 e V1, for each i = 1,2,...,m.

Definition 12 (Ghorai and Pal 2016a) G = (V, A, B) is called strong if Pi o B(xy) = min{pi o A(x),pi o A(y)} for all xy e E, i = 1,2,..., m.

A strong m-polar fuzzy graph G is called self complementary if G = G. Degree of a vertex in an m-polar fuzzy graph is defined as below.

Definition 13 (Akram and Younas 2015) The neighborhood degree of a vertex v in the m-polar fuzzy graph G is denoted as deg(v) = (p1 o deg(v),p2 o deg(v),...,pm o deg(v)) where p,- o deg(v) = J] u = v Pi ◦ B(uv), i = 1,2,..., m. uv e E

Remark 14 If G1 = (Vl,A1, B1) and G2 = (V2,A2, B2) are two m-polar fuzzy graphs. Then the canonical projection maps n1 : V1 x V2 ^ V1 and n2 : V1 x V2 ^ V2 are indeed homomorphisms from G1 x G2 to G1 and G1 x G2 to G2 respectively. This can be seen as follows:

Pi o (Ai xÄ2)(xi,x2) = min{pi oAi(xi),pt o^2(^2)} < pi oAi(xi) = pt oAi(ni(xi,X2)) for all (xi,X2) e Vi x V2 andp,- o (Bi x B2)((x 1,z)(yi,z)) = mlwjpi o Bi(xiyi),pi o A2(z)} < Pi o B1(x1y1) = pi o B1(n1(x1, z)n1(y1, z)) for all z e V2 and x1y1 e E1. In a similar way we can check the other conditions also. This shows that the canonical projection maps n1 : V1 x V2 ^ V1 is a homomorphism from G1 x G2 to G1.

Weak self complement m-polar fuzzy graphs

Self complement m-polar fuzzy graphs have many important significant in the theory of m-polar fuzzy graphs. If an m-polar fuzzy graph is not self complement then also we can say that it is self complement in some weaker sense. Simultaneously we can establish some results with this graph. This motivates to define weak self complement m-polar fuzzy graphs.

Definition 15 Let G = (V,A, B) be an m-polar fuzzy graph of the crisp graph G* = (V, E ). The complement of G is an m-polar fuzzy graph G = (V,A,B) of G* = (V, V2) such that A = A ^and B is defined by Pi o B(xy) = min{pi o A(x),pi o A(y)} — pi o B(xy) for xy e V2, i = 1,2,..., m.

Example 16 Let G = (V, A, B) be a 3-polar fuzzy graph of the graph G* = (V, E) where v = {u, v, w, *}, E = {uv, VW, WU, ux}, A = f i0.2,0^, i0.5,0^, i0.7,0.2,0.3L, \

I u v w x I

B = {■^^,i040W01L,,, ^, <0^ }• Then by Definition 15, we have constructed the complement G of G which is shown in Fig. 1.

Remark 17 Let G = (V, A, B) be the complement of G where A = A = A and

Pi o B(uv) = min{pi o A(u),pi o A(v)} — pi o B(uv)

= min{pi o A(u),pi o A(v)} — {min{pi o A(u),pi o A(v)} — pi o B(uv)}

= pi o B(uv) for uv e V2, i = 1,2,..., m.

Hence, G = G.

Definition 18 The m-polar fuzzy graph G = (V,A, B) is said to be weak self complement if there is a weak isomorphism from G onto G. In other words, there exist a bijec-tive homomorphism <p : G ^ G such that for i = 1,2,..., m

(i) pi o A(u) = pi o A(<(u)) for all u e V,

(ii) pi o B(uv) < pi o B(< (u)< (v)) for all uv e V2.

Example 19 Let G = (V,A, B) be a 3-polar fuzzy graph of the graph G* = (V,E) where V = {u, v, w}, E = {uv, vw}, A = {(OMiM), i02^, ■o^^l,

i u V w i

B = {((n,o.i,a2), <o.i,a.2,o.2), <cmj. Then g = (V,A, B) is also a 3-polar fuzzy graph

where A = A and B = f <010302L, <010305L, <03WUML \ We can easily verify that, the

identity map is an weak isomorphism from G onto G(see Fig. 2). Hence G is weak self complement.

In Ghorai and Pal (2015a), Ghorai and Pal proved that if G is a self complementary strong m-polar fuzzy graph then for all xy e V2 and i = 1,2,..., m

< 0.3, 0.4, 0.4 > < 0.2, 0.5, 0.7 >< 0.3, 0.4, 0.4 > < 0-2. 0-5> 0-7 >

v u ^- v

< 0.1, 0.1, 0.2 >

< 0.1, 0.2, 0.2 >

< 0.3, 0.6, 0.7 >

< 0.3, 0.4, 0.4 >

< 0.1, 0.3, 0.5 >

< 0.3, 0.6, 0.7 >

Fig. 2 Weak self complement 3-polar fuzzy graphs

YsPi ◦ B(xy) = 2 X] min{pi ° A(x),Pi ◦ A(y)}.

x=y x=y

The converse of the above result does not hold always.

Example 20 For example, let us consider a 3-polar fuzzy graph G = (V,A,B) of G* = (V, E) whereV = {u, v, w}, E = {uv, vw, wu}, A = { , (M0506), i0507^ },

B = { ,, }. Then we have the following

pi о B(uv) + pi о B(vw) + pi о B(wu) = 0.2 + 0.1 + 0.1 = 0.4 and 2 [min{p1 о A(u), p1 о A(v)} + min{p1 о A(v), p1 о A(w)} + min{p1 о A(w), p1 о A(u)}]

= 2 [min{0.2,0.4} + min{0.4,0.5} + min{0.5,0.2}] = 2 (0.2 + 0.4 + 0.2) = 0.4.

о B(uv) = 0.4 = - ^ min{p1 о A(u),pi о A(v)}.

u=v u=v

Similarly,

У^р2 о B(uv) = 0.55 = - ^ min{p2 о A(u),p2 о A(v)}

u=v u=v

y^p3 о B(uv) = 0.7 = - ^ min{p3 о A(u),p3 о A(v)}.

u=v u=v

Hence for i = 1,2,3 we have,

^Pi o B(uv) = 2 min{pi o A(u),Pi o A(v)}.

u=v u=v

But G is not self complementary as there exists no isomorphism from G onto G (see Fig. 3).

Now suppose an m-polar fuzzy graph G = (V, A, B) is a weak self complement. Then the following inequality holds.

Theorem 21 Let G = (V, A, B) be a weak self complement m-polar fuzzy graph of G*. Then for i = 1,2,..., m

° B(xy) - 2 min{pi ° A(x),Pi ° A(y)}

x=y x=y

Proof Since G is weak self complement, therefore there exists a weak isomorphism <p : V ^ V such that pi o A(x) = pi o A(<(x)) for all x e V and Pi o B(xy) < pi o B(< (x)<(y)) for all xy e V2, i = 1,2,..., m.

Using the above we have,

Pi o B(xy) < pi o B(< (x)< (y)) = min{pi o A(x),pi o A(y)} - pi o B(< (x)< (y)) i.e.,pi o B(xy) + pi o B(<(x)<(y)) < min{pi o A(<(x)),pi o A(<(y))}.

Therefore, for all xy e V2, i = 1,2,..., m o B(xy) + Pi ◦ B(<p(x)<p(y)

x=y x=y

min{pi o A(<p(x)),Pi o A(<p(y))}

= ^ min{pi o A(x),Pi o A(y)}

< 0.2, 0.3, 0.4 > u

< °.4, 0.5, 0.6 > < 0.2, 0.3, 0.4 > v

< 0.1, 0.2, 0.2 >

< 0.1, 0.05, 0.1 >

< 0.4, 0.5, 0.6 > v

< 0. 3, 0. 3, 0. 4 >

< 0.1, 0.25, 0.3 >

< 0.5, 0.7, 0.8 > _

Fig. 3 Example of 3-polar fuzzy graph G which is not self complement

< 0.5,0.7,0.8 >

2^pi o B(xy) < ^min{pi o A(x),p, o A(y)}

X=y x=y

J^Pi o B(xy) < 2 ^min{pi o A(x),Pi o A(y)}.

x=y x=y

Remark 22 The converse of the above theorem is not true in general. For example, consider the 3-polar fuzzy graph of Fig. 3. We see that for the 3-polar fuzzy graph G, the condition of Theorem 21 is satisfied. But, G is not weak self complementary as there is no weak isomorphism from G onto G.

Theorem 23 If pi o B(xy) < 2min{pi o A(x),pi o A(y)} for all xy e V2, i = 1,2,..., m then G is a weak self complement m-polar fuzzy graph.

Proof Let G = (V, A, B) be the complement of G where A(x) = A(x) for all x e V and pi o B(xy) = min{pi o A(x),pi o A(y)} — pi o B(xy) for xy e V2, i = 1,2,..., m.

Let us now consider the identity map I: V ^ V. Then A(x) = A(I(x)) = A(I(x)) for all x e V and

Pi o B(I(x)I(y)) = pi o B(xy)

= min{pi o A(x),pi o A(y)} — pi o B(xy)

> min{pi o A(x),pi o A(y)} — ^min{pi o A(x),pi o A(y)} = 2min{pi o A(x),pi o A(y)} > pi o B(xy).

So, pi o B(xy) < pi o B(I(x)I(y)) for i = 1,2,..., m and xy e V2. Hence, I: V ^ V is a weak isomorphism. □

Example 24 Consider the 3-polar fuzzy graph G = (V,A, B) of G* = (V,E) where V = {u, v, w}, E = [uv,vw, wu}, A = {¡MM^, i0«, ),

{-v I U v w ^L

¡"WW)., ¡^im, ¡oiAM2) j. We see that for each i = 1,1,3 and xy e V2,

uv vw wu I

Pi o B(xy) < 1 min{pi o A(x),Pi o A(y)} .

Also, consider the complement of G of Fig. 4. Let us now consider the identity mapping I: G ^ G such that I(u) = u for all u e V. Then, I is the required weak isomorphism from G onto G. Hence, G is weak self complementary.

Order, size and busy value of vertices of m-polar fuzzy graphs

In this section, the order, size, busy value of vertices of an m-polar fuzzy graph is defined.

< 0.2, 0.3, 0.4 > < 0.4, 0.5, 0.6 > < 0.2, 0.3, 0.4 >

< 0.2, 0.2, 0.3 >

< 0.1, 0.2, 0.2 >

< 0.1, 0.1, 0.2 >

< 0.4, 0.5, 0.6 > t v

< 0.2,0.3,0.3 :

< 0.5, 0.7, > < 0.5, 0.7, 0.9 >

Fig. 4 Example of 3-polar fuzzy graph G which is weak self complement

Definition 25 The order of the m-polar fuzzy graph G = (V,A,B) is denoted by |V| (or O(G)) where

v^ 1 + Efi Pi ◦ A(x) O(G) = | V | = Y,-——.

The size of G is denoted by |£| (or S(G)) where 1 + YZi Pi ◦ B(xy)

S(G) = \E\=J2

Theorem 26 Two isomorphic m-polar fuzzy graphs Gi = (Vi,Ai, Bi) and G2 = (V2,A2,B2) of the graphs G^ = (Vi,Ei) and = (V2,E2) have same order and size.

Proof Let <f> be an isomorphism from G1 onto G2. Then A1(x) = A2(<(x)) for all x e V1 and pi o B1 (xy) = pi o B2 (< (x)< (y)) for i = 1,2,..., m, xy e V2.

0(Gl) = |Vi\ = E 1 + S='f ◦ Ai(x)

= E 1 + EM=1 Pi ◦ A2(<(x)) = 0(G)

<(X)SV2 2

S(Gl) = |£l\= E 1 + sv- ◦ Bl(xY)

= E 1 + EM=1 P- ◦ B2(<(x)<(y)) = S(G2)

<(x)<(y)eE2 2

< 0.6, 0.3, 0.5 > < 0.5, 0.2, 0.2 > < 0.8, 0.4, 0.3 >

< 0.6, 0.2, 0.4 >

< 0.1, 0.3, 0.2 >

< 0.7, 0.5, 0.6 >

< 0.5, 0.6, 0.4 >

Fig. 5 3-Polar fuzzy graph G and busy value of its vertices

Definition 27 The busy value of a vertex u of an m-polar fuzzy graph G is denoted as D(u) = (pi o D(u),p2 o D(u),...,pm o D(u)) where

Pi o D(u) = Y1 min{pi o A(u),pi o A(uk)}; Uk are the neighbors of u. The busy value of G is

denoted as D(G) where D(G) = D(uk), Uk e V.

Example 28 Consider the 3-polar fuzzy graph G = (V,A, B) of G* = (V,E) where V = {u, v, w, x}, E = {uv, vw, ux, uw, vx}, A = { (a6'°ufa5>, W4^,

(0.5,0.6,0.4) (0.7,0.5,0.6) 1 d g = ( (0.5,0.2,0.2) (0.1,0.3,0.2) (0.6,0.2,0.4) (0.3,0.2,0.3) (0.7,0.4,0.2) ) w ' x J 1 uv , vw , ux , uw , vx y

Then we have from Fig. 5,

pi o D(u) = 1.7, p2 ◦ D(u) = 0.9, p3 o D(u) = 1.2,

pi o D(v) = 1.8, p2 o D(v) = 1.1, P3 o D(v) = 0.9,

p1 o D(w) = 1, p2 o D(w) = 0.7, p3 o D(w) = 0.7,

p1 o D(x) = 1.3, p2 o D(x) = 0.7, p3 o D(x) = 0.8.

So, D(u) = (1.7,0.9,1.2), D(v) = (1.8,1.1,0.9), D(w) = (1,0.7,0.7), D(x) = (1.3,0.7,0.8).

Definition 29 If pi o A(u) < pi o deg(u) for i = 1,2,..., m, then the vertex u of G is called a busy vertex. Otherwise it is a free vertex.

Definition 30 If pi o B(u\v\) = min{pi o A(u{), pi o A(vi)}, i = 1,2,..., m for u1v1 e E, then it is called an effective edge of G.

Definition 31 Let u e V be a vertex of the m-polar fuzzy graph G = (V, A, B).

(i) u is called a partial free vertex if it is a free vertex of G and G.

(ii) u is called a fully free vertex if it is a free vertex of G and it is a busy vertex of G.

(iii) u is called a partial busy vertex if it is a busy vertex of G and G.

(iv) u is called a fully busy vertex if it is a busy vertex in G and it is a free vertex of G.

Theorem 32 Let <p be an isomorphism from G1 = (V1, A1, B1) onto G2 = (V2, A2, B2). Then deg (u) = deg (<^(u)) for all u e V1.

Proof Since <is an isomorphism between G1 and G2, we have pi o A\(u) = Pi ◦ A2(<(u)) for all u e Vl and pi o Bi(xiyi) = pi o B2(<(xi)<(yi)) for all xiyi e V2, i = 1,2,..., m.

Hence,pi ◦ deg(u) = E u = v Pi ◦ Bi(uv) = E <(u) = <(v) Pi ◦ B2(<(u)<(v)) = uv e E\ <(u)<(v) e E2

Pi o deg(<(u)) for u e Vl, i = 1,2,..., m. So, deg(u) = deg(<(u)) for all u e V1. □

Theorem 33 If < is an isomorphism from Gl onto G2 and u is a busy vertex of Gl, then <p (u) is a busy vertex of G2.

Proof Since < is an isomorphism between we have, pi o A1(u) = pi o A2(<(u)) u e Vl and pi o Bi(xiyi) = pi o B2(<(xi)<(yi)) for xiyi e V2, i = 1,2,..., m.

If u is a busy vertex of Gl, then pi o A1 (u) < pi o deg (u) for i = 1,2,..., m. Then by the above and Theorem 32, pi o A2(<(u)) = pi o A1(u) < pi o deg(u) = pi o deg(<(u)) for i = 1,2,..., m. Hence, <p (u) is a busy vertex in G2. □

Theorem 34 Let the two m-polar fuzzy graphs G1 and G2 be weak isomorphic. If u e Vl is a busy vertex of G1, then the image of u under the weak isomorphism is also busy in G2.

Proof Let <p : Vi ^ V2 be a weak isomorphism between G1 and G2.

Then, pi o Ai(x) = pi o A2(<(x)) for all x e Vi and pi o Bi(xiyi) < pi o B2(<(xi)<(yi)) for all x1y1 e V2, i = 1,2,..., m. Let u e V1 be a busy vertex. Then, for i = 1,2,..., m, pi o AL(u) < pi o deg(u). Now by the above for i = 1,2,..., m

Pi o A2 (u) = pi o Ai (u) — pi o deg (u) = pi o Bi (uv)

u = v uv e E1

- Pi o B2(<(u)<(v)) = Pi o deg(<(u)).

<(u) = <(v) <(u)< (v) e E2

Hence, <p (u) is a busy vertex in G2. □

Complement and isomorphism in m-polar fuzzy graphs

In this section some important properties of isomorphism, weak isomorphism, co weak isomorphism related with complement are discussed.

Theorem 35 Let Gi = (Vi, Ai, Bi) and G2 = (V2, A2, B2) be two m-polar fuzzy graphs of the graphs Gjf = (Vi, Ei) and G| = (V2, £2). If Gi = G2 then Gi = ~G2.

Proof Let Gi = G2. Then there exists an isomorphism < : Vi ^ V2 such that Ai(x) = A2(<(x)) for ^ll x e Vl and pi o Bi(xy) = pi o B2(<(x)<(y)), for each i = 1,2,..., m and xy e Vj2.

Now, Ai(x) = Ai(x) = A2(<£(*)) = A2(<p(x)) for all x e Vi. Also, for i = 1,2,..., m and xy e V2 we have,

pi o Bi(xy) = min{pi o Ai(x),pi o Ai(y)} -pi o Bi(xy)

= min{pi o A2((p(x),pi o A2((p(y)} - pi o B2((p(x)(p(y)) = pi o B2(<p(x)<p(y)).

Hence, <f> is an isomorphism between G1 and G2 i.e., G1 = G2. □

Remark 36 Suppose there is a weak isomorphism between two m-polar fuzzy graphs G1 and G2. Then there may not be a weak isomorphism between G1 and G2.

For example, consider two 3-polar fuzzy graphs G1 and G2 of Fig. 6. Let us now define a mapping <p : V1 ^ V2 such that <p (a) = u, <p (b) = v, <p (c) = w. Then <p is a weak isomorphism from G1 onto G2. But, there is no weak isomorphism from G1 onto G2 (see Fig. 7) because B2(uw = <p(a)<p(c)) = 0 = (0,0,..., 0) < B[(ac) = (0.1,0.1,0.05), and B2(vw = <p(b)<p(c)) = 0 = (0,0,..., 0) < Bl(bc) = (0.1,0.1,0.1).

Remark 37 In a similar way, we can construct example to show that if there is a co-weak isomorphism between two m-polar fuzzy graphs G1 and G2 then there may not be a co-weak isomorphism between G1 and G2.

< 0.6,0.8,0.1 > < 0,5; 0,7; 0,2 > < 0.6,0.8,0.1 >

a •-a b up

< 0.1, 0.1,0.05 >

< 0.1, 0.5, 0 >

/< 0.1, 0.1, 0.1 >

< 0.1, 0.4, 0 >

< 0.4, 0.3, 0.2 > • w

< 0.4, 0.3, 0.2 >

< 0.5, 0.7, 0.2 >

Fig. 7 Example of weak isomorphic graphs whose complement is not weak isomorphic

Theorem 38 Let Gi = (Vi,Ai, Bi) and G2 = (V2,A2, B2) be two m-polar fuzzy graphs of the graphs = (Vi,Ei) and G| = (V2,E2) such that Vi n V2 = 0. Then Gi + G2 = Gi U G2.

Proof To show that G1 + G2 = G1 U G2, we need to show that there exists an isomorphism between Gi + G2 and G1 U G2.

We will show that the identity map I: Vi U V2 ^ Vi U V2 is the required isomorphism between them. For this, we will show the following: for all x e Vi U V2, JAiTA2)(x) = (A U A2)(x),

and pi o (B1 + B2)(xy) = pi o (B1 U B2)(xy) for i = 1,2,..., m and xy e V x V2 .

Let x e Vi U V2.

(Ai + A2)(x) = (Ai + A2)(x) = (Ai U A2)(x) (by Definition 8)

A1(x) if x e V2 - V2 A2(x) if x e V2 - V1

f A1(x) if x e V1 - V2 ,-r-, , -¡-N, . = { A2(x) ifx e V2 - V, = (Ai U A2)(X).

Now for each i = 1,2,..., m and xy e Vi x V2 we have,

Pi o (Bi + B2)(xy)

= ot1m(p; o (Ai + A2)(x),Pi o (Ai + A2)(y)} - pi o (Bi + B2)(xy)

( Mi'wjpi o (Ai U A2)(x), Pi o (Ai U A2)(y)} - Pi ◦ (Bi U B2)(xy), if xy e £1 U £2

\ mi'w(pi o (Ai U A2)(x), pi o (Ai U A2)(y)} - Mi'w(pi o Ai (x), pi o A2O)}, if xy e £'

{OTi'w(pi o Ai (x) ,pi o Ai (y)} - pi o Bi (xy), if xy e £i - £2

OTi'w(pi o A2 (x) ,pi o A2 (y)} - pi o B2 (xy), if xy e £2 - £i

OTi'w(pi o Ai(x), pi o A2(y)} - OTi'w(pi o (Ai)(x), pi o (A2)(y)}, if xy e £'

{Pi o Bi(xy), if xy e £i - £2 Pi o B2 (xy), if xy e £2 - £i 0, if xy e £'

= pi o (Bi UB2)(xy).

Theorem 39 Let Gi = (Vi,Ai, Bi) and G2 = (V2,A2, B2) be two m-polar fuzzy graphs of the graphs = (Vi,Ei) and G| = (V2,E2) such that Vi n V2 = 0. Then Gi U G2 = Gi + G2.

Proof Consider the identity map I: Vi U V2 ^ Vi U V2. We will show that I is the required isomorphism between G1 U G2 and G1 + G2.

For this, we will show the following:

for all x e Vi U V2, (ATuAXx) = (A + A2)(x),

and pi o (Bi U B2)(xy) = pi o (Bi + B2)(xy) for i = 1,2,..., m and xy e V x V2 .

Let x e Vi U V2.

Ai U A2(x) = (Ai U A2)(x)

f Ai(x), if x e Vi - V2 = ( A2(x), if x e V2 - Vi

i AT(x), if x e Vi - V2 I A2(x), if x e V2 - Vi

= (Ai U A2)(x)

and for i = 1,2,..., m, xy e V x V2 we have,

Pi o (Bi U B2)(xy)

= o (Ai U A2)(x),Pi o (Ai U A2)(y)} -p, o (Bi U B2)(xy)

{m/«{p,- o Ai (x),pi o Ai (y)} - pi o Bi (xy), if xy e £1 - £2 min{pi o A2 (x),p, o A2 (y)} - pi o B2 (xy), if xy e £2 - £i min{pi o Ai(x),p, o A2 (y)} 0, if x e Vi,y e V2

{pi o Bi(xy), if xy e £i - £2

pi o B2 (xy), if xy e £2 - £i

min{pi o Ai(x),p, o A2 (y)} 0, if x e Vi,y e V2

(pi o Bi(xy), if xy e £i - £2

pi o B2 (xy), if xy e £2- £i

wi^p, o Ai (x),p, o A2 (y)} - 0, if xy e £'

= p,- o (Bi + B2)(xy).

This completes the proof. □

Theorem 40 Let G1 = (V1,A1, B1) and G2 = (V2,A2, B2) be two strong m-polar fuzzy graphs of the graphs G* = (V1, E1) and G* = (V2, E2) respectively. Then Gi o G2 = Gi o G2.

Proof Let G1 o G2 — (Vi x V2, Ai o A2, Bi o B2) be an m-polar fuzzy graph of the graph G* = (V,E) where V = V1 x V2 and E = {(x,x2)(x,y2) : x e V1,x2y2 e E2} U {(xi,z)(y1,z) : z e V2,xiyi e £1} U {(xi,X2)(yi,y2) : xiyi e Ei,X2 = y2}.

We show that the identity map I is the required isomorphism between the graphs

G1 o G2 and G1 o G2. Let us consider the identity map I: V1 x V2 ^ V1 x V2.

In order to show that I is the required isomorphism, we show that for each i = 1,2,..., m and for all xy e V x V2 , p, o (B1 o B2)(xy) = p, o (B1 o B2)(xy). Several cases may arise.

Case (i): Let e = (x,x2)(x,y2) where x e Vl, x2y2 e E2. Then e e E.

Since G1 o G2 is strong m-polar fuzzy graph, we have for each i = 1,2,..., m

Pi o (B1 o B2)(e) = 0 and

Pi o (Bi o B2)(e) = min{pi o Ai(x),pi o B2(x2j2)} = 0

(since G2 is strong and x2y2 e E2, therefore for each i = 1,2,..., m, p, o B2 (x2y2) = 0). Case (ii): Let e = (x, x2)(x,y2) where x2 = y2, x2y2 / E2. Then e / E.

So for each i = 1,2,..., m, p o (B1 o B2)(e) = 0 and

pi o (Bi o B2)(e) = min{pi o (Ai o A2)(x,X2),pi ◦ (Ai o A2)(x,y2)}

= min{pi o Ai(x),pi o A2(X2),Pi o A2CK2)}. Again, since x2y2 e E2, therefore for each i = 1,2,..., m,

pi o (Bi o B2)(e) = min{pi o Ai(x),pi o B2(X2y2)}

= min{pi o Ai(x),pi o A2(X2),pi o A2(y2)}.

Case (iii): Let e = (xi, z)(yi, z) where x1y1 e E1 z e V2.

Then e e E. So for each i = 1,2,..., m, pi o (B1 o B2)(e) = 0 as in Case (i). Also, since x1y1 / £1, therefore for each i = 1,2,..., m, p o (B1 o B2)(e) = 0.

Case (iv): Let e = (xi, z)(yi, z) where xLyL / El, z e V2. Then e / E.

Hence for each i = 1,2,..., m, p o (B1 o B2)(e) = 0,

pi o (Bi o B2)(e) = min{pi o (Ai o A2)(xi,z),pi o (Ai o A2)(yi,z)}

= min{pi o Ai(xi),pi o Ai(yi),pi o A2(z)} and pi o (Bi o B2)(e) = min{pi o A2(z),pi o Bi(xiyi)}

= min{pi o Ai(xi),pi o Ai(yi),pi o A2(z)} (Gi being strong).

Case (v): Let e = (xi, x2)(yi, y2) where x1 y1 e E1, x2 = y2. Then e e E. So we have for each i = 1,2,..., m, p o (B1 o B2)(e) = 0 as in Case (i).

Also, since x1y1 e E1, we have for each i = 1,2,..., m, p o (B1 o B2)(e) = 0.

Case (vi): Let e = (xi, x2)(yi,y2) where x1y1 / £1, x2 = y2. Then e / E and hence for

each i = 1,2,..., m, p o (B1 o B2)(e) = 0,

pi o (Bi o B2)(e) = min{pi o (Ai o A2)(xi,x2),pi o (Ai o A2)(yi,y2)}

= min{pi o Ai(xi),pi o Ai(yi),pi o A2(x2),pi o A2(y2)}

and since x1y1 e E1,

pi o (Bi OB2)(e) = o A2(X2),PI O^2(72),PI OBI(XIYI)}

= OTi«{p; O Ai(xi),pi o Ai(7i),pi o a2(x2),pi o^2(72)} (Gi being strong by [10]).

Case (vii): Finally, let e = (x1, x2)(y1,y2) where x1y1 / £1, x2y2 / E2. Then e / E and hence for each i = 1,2,..., m, p o (B1 o B2)(e) = 0,

pi o (Bi o B2) (e) = min{pi o (Ai o A2)(xi, x2),pi o (Ai o A2)(yi,y2)}.

Now, x1y1 e E1 and if x2 = y2 = z, then we have the Case (iv). Again, if x1y1 e E1 and if x2 = y2, then we have Case (vi). Thus__copbining all the cases we have, for each i = 1,2,..., m, and xy e V x V2 ,

pi o (Bi o B2)(xy) = pi o (Bi o B2)(xy).

Remark 41 If G1 and G2 are not strong, then G1 o G2 ^ G1 o G2 always. For example, consider the two 3-polar fuzzy graphs G1 and G2 which are not strong (see Fig. 8). From Figs. 8 and 9, we see that, G1 o G2 ^ G1 o G2.

Applications

Now a days, fuzzy graphs and bipolar fuzzy graphs are most familiar graphs to us and they can also be thought of as 1-polar and 2-polar fuzzy graphs respectively. These graphs have many important application in social networks, medical diagnosis, computer networks, database theory, expert system, neural networks, artificial intelligence, signal

< 0.2, 0.5, 0.3 >< 0.4, 0.6, 0.2 > < 0.2, 0.5, 0.2 > < 010 0 > < °-2> °-2> 0.1 >

(a, c) fv-—-Tf (a, d)

< 0, 0.1, 0.1 >

< 0.2, 0.2, 0 >

< 0, 0.1, 0.1 >

< 0, 0.1,

(b, d)

< 0.3, 0.6, 0.4 > < 0.5, 0.2, 0.1 > < 0.3, 0.6, 0.2 > < 0.1, 0 0 > < 0.3, 0.2, 0.1 >

Gi o G2

Fig. 9 Example of 3-polar fuzzy graphs G1 and G2 where G1 o G2 ^ G1 o G2

processing, pattern recognition, engineering science, cluster analysis, etc. The concepts of bipolar fuzzy graphs can be generalized to m-polar fuzzy graphs. For example, consider the sorting of mangoes and guavas. Now the different characteristics of a given fruit can change the decision in sorting process more towards the decision mango or vice versa. There are two poles present in this case. One is 100% sure mango and the other is 100% sure guava. This shows that the situation is bipolar. This situation can be generalized further by adding a new fruit, for example sweet lemon into the sorting process.

Graphical representation of tug of war

Consider the another example of tug of war where two people pull the rope in opposite directions. Here, who uses the bigger force, the center of the rope will move in the respective direction of their pulling. The situation is symmetric in this case. We present an example where m people pull a special rope in m different directions. We use this example to represent it as an m-polar fuzzy graph. We assume that O is the origin and there are m straight paths leading from O. We also assume that there is a wall in between these paths. In this setting, we have the special rope with one node at O and m endings going out from this nodes—one end corresponding to each of the paths. Suppose on every path there is a man standing and pulling the rope in the direction of the path on which he is standing. This situation can be represented as an m-polar fuzzy graph by considering the nodes as m-polar fuzzy set and edges between them as m-polar fuzzy relations, which is shown in Fig. 10. In this context, one can ask the question what is the strength require in order to pull the node O from the center into one of the paths (assuming no friction)? The answer to this is that if the corresponding forces which are pulling the rope are Fk, k = 1,2,..., m, then the node O will move to the jth path if

Evaluation graph corresponding to the teacher's evaluation by the students

In this section we present the model of m-polar fuzzy graph which is used in evaluating the teachers by the students of 4th semester of a department in an university during the session 2015-2016. Here the nodes represent the teachers of the corresponding department and edges represent the relationship between two teachers. Suppose the department has six teachers denoted as T = {t1, t2, t3, t4, t5, t6}. The membership value of each node represents the corresponding teachers feedback response of the students depending

Fj > E k = 1,2,...,m Fk

Fig. 10 Graphical representation of tug of war

on the following: {regularity of classes, style of presentation, quality of lectures, generation of interest and encouraging future reading among students, updated information}. Since all the above characteristics of a teacher according to the different students are uncertain in real life, therefore we consider 5-polar fuzzy subset of the vertex set T (Fig. 11).

In the Table 1, the membership values of the teacher's are given which is according to the evaluation of the students.

Edge membership values which represent the relationship between the teachers can be calculated by using the relation pi o B(uv) < min{pi o A(u),pi o A(v)} for all u, v e T, i = 1,2,..., 5. These values are given in the Table 2.

We rank the teacher's performance according the following:

Teacher's average response score <60%, teacher's performance according to the students is Average.

Teacher's average response score >60% and <70%, teacher's performance according to the students is Good.

Teacher's average response score >70% and <80%, teacher's performance according to the students is Very Good.

Teacher's average response score is >80%, teacher's performance according to the students is Excellent.

From the Table 3, we see that the performance of the teachers t1, t2, t5, t6 are very good whereas the performance of the teachers t3 and t4 are excellent. Among these teachers, teacher t3 is the best teacher according the response score of the students of the department during the session 2015-2016.

t2(0.7, 0.6, 0.7, 0.8, 0.8) (°.6, °.6, °.7, °.8) t3(0.8, 0.9,0.7, 0.8,0.9) ^(0.8, 0.7, 0.6, 0.6,0.8) / ^^

(0.6, 0.6,0.7, 0.8,0.7)/ \ ^^ \(0.8,0.7,0.7,0.8 , °.8)

(0.7,0.5, 0.6, 0.7, 0.6) (0.7, 0.5, 0.7, 0.7, 0.8)

t1(0.6, 0.7,0.8 / ' (0.5,0.7, 0.7,0.6, 0.8) (0.8,0.7,0.7,0.7,0.8) t4(0.8, 0.7, 0.8, 0.9, 0.8) 0.7, 0.7, 0.7)

\is(0.8, 0.9,0.7, 0.7,0.8) / (0.7, 0.8, 0.6, 0.6, 0.7) ^ , , , , ' /(0.6,0.7

(0.6,0.7,0.8,0.7,0.8) ^^^ /

- i6(0.7, 0.8, 0.9,0.7 0. 8)

Fig. 11 5-Polar fuzzy evaluation graph corresponding to the teacher's evaluation by students I. J

Table 1 5-Polar fuzzy set A of T

fl f2 f3 f4 f5 f6

pi O a 0.6 0.7 0.8 0.8 0.8 0.7

p2 o a 0.7 0.6 0.9 0.7 0.9 0.8

p3 O a 0.8 0.7 0.7 0.8 0.7 0.9

p4 o A 0.9 0.8 0.8 0.9 0.7 0.7

p5 O a 0.9 0.8 0.9 0.8 0.8 0.8

Table 2 5-Polar fuzzy relation B on A

tl t2 il t5 tl t6 t213 t214 t215 t314 t315 t415 t416 t516

pi ◦ a 0.6 0.5 0.6 0.6 0.8 0.7 0.8 0.7 0.8 0.6 0.7

p2 o a 0.6 0.7 0.7 0.6 0.7 0.5 0.7 0.5 0.7 0.7 0.8

p3 o a 0.7 0.7 0.8 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.6

p4 o a 0.8 0.6 0.7 0.7 0.6 0.7 0.8 0.7 0.7 0.7 0.6

p5 o a 0.7 0.8 0.8 0.8 0.8 0.6 0.8 0.8 0.8 0.7 0.7

Table 3 Average response score of the teachers

Teachers

tl t2 t3 U is t6

Scores 0.78 0.72 0.82 0.8 0.78 0.78

Conclusions

The theory of fuzzy graphs play an important role in many fields including decision makings, computer networking and management sciences. An m-polar fuzzy graph can be used to represent real world problems which involve multi-agent, multi-attribute, multi-object, multi-index, multi-polar information and uncertainty. In this research paper, we have studied the isomorphic properties of m-polar fuzzy graphs with some applications. We are extending our research work on m-polar fuzzy intersection graphs, m-polar fuzzy interval graphs, properties of m-polar fuzzy hypergraphs, degrees of vertices of m-polar fuzzy graphs and its application in decision making, etc.

Authors' contributions

Both authors have significant contributions to this paper and the final form of this paper is approved by both of them. Both authors read and approved the final manuscript.

Acknowledgements

The authors would wish to express their sincere gratitude to the Editor in Chief and anonymous referees for their valuable comments and helpful suggestions.

Competing interests

The authors declare that they have no competing interests.

Received: 14 July 2016 Accepted: 1 December 2016 Published online: 20 December 2016

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