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 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 . For example, the exact degree of telecommunication safety of mankind is a point in 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 , similarity degrees of two logic formula which are based on n logic implication operators , 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 set in a universe X is a mapping . A fuzzy relation on X is a fuzzy set in . Let be a fuzzy set in X and fuzzy relation on X. We call is a fuzzy relation on if .
Recently, Akram and Akmal (2016) applied the concept of bipolar fuzzy sets to graph structures.
Definition 2
(Akram and Akmal 2016) is called a bipolar fuzzy graph structure(BFGS) of a graph structure (GS) if is a bipolar fuzzy set onU and for each is a bipolar fuzzy set on such that
Note that for all and , where U and are called underlying vertex set and underlying i-edge sets of , respectively.
Definition 3
(Akram and Akmal 2016) Let be a BFGS of a GS Let be any permutation on the set and the corresponding permutation on i.e., if and only if
If for some r and
then while m is chosen such that
And BFGS denoted by is called the -complement of BFGS
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 -set) on X is exactly a mapping
Note that (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 when ), is defined by for each ( , and is the ith projection mapping . is the smallest element in and is the largest element in . 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 defined by the mapping such that for all where denotes the ith degree of membership of the vertex x and 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 , where is an m-polar fuzzy set in V and is an m-polar fuzzy relation on V such that
for all
We note that for all for all 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 for all
m-Polar fuzzy graph structures
We first define the concept of an m-polar fuzzy graph structure.
Definition 7
Let be a graph structure (GS). Let C be an m-polar fuzzy set on U and an m-polar fuzzy set on such that
for all , and for . Then is called an m-polar fuzzy graph structure (m-PFGS) on where C is the m-polar fuzzy vertex set of and is the m-polar fuzzyi-edge set of .
We illustrate the concept of an m-polar fuzzy graph structure with an example.
Example 8
Consider a graph structure such that , and Let C, and be 4-polar fuzzy sets on and , respectively, defined by the following tables:
| C | ||||
|---|---|---|---|---|
| 0.1 | 0.3 | 0.4 | 0.2 | |
| 0.0 | 0.6 | 0.0 | 0.0 | |
| 0.0 | 0.2 | 0.4 | 0.3 | |
| 0.1 | 0.0 | 0.4 | 0.4 |
| 0.1 | 0.2 | 0.2 | ||
| 0.0 | 0.0 | 0.0 | ||
| 0.0 | 0.2 | 0.2 | ||
| 0.0 | 0.0 | 0.0 | ||
By simple calculations, it is easy to check that is a 4-polar fuzzy graph structure of as shown in Fig. 1. Note that we represent as in all tables and the figures.
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 and be two m-PFGSs. Then the Cartesian product of and is given by
where the mappings and (for ) are respectively defined by
and
where j varies from 1 to m.
We illustrate Cartesian product of and with an example.
Example 10
Let be a 4-PFGS of graph structure where and is drawn and shown in the Fig. 2.
Fig. 2.

4-Polar fuzzy graph structure
The Cartesian product of (Fig. 1) and given by is as shown in Fig. 3. In the figure, a -edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.
Fig. 3.

Cartesian product of two 4-PFGSs
We now formulate Cartesian product of and as a proposition.
Proposition 11
Cartesian product of twom-polar fuzzy graph structures is anm-polar fuzzy graph structure.
Proof
Let GS be the Cartesian product of GSs and Let and be respective m-PFGSs of and Then is an m-PFGS of By the Definition 9 of Cartesian product, is an m-polar fuzzy set of and is an m-polar fuzzy set of for all i. So the remaining task is to prove that is an m-polar fuzzy relation on for all i. For this, some cases are discussed, as follows:
Case 1. When and
Case 2. When
Both cases hold for every This completes the proof.□
We define cross product of and by an example.
Definition 12
Let and be two m-PFGSs. Then the cross product of and is given by
where the mappings and (for ) are respectively defined by
and
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 and shown in the Figs. 1 and 2, respectively. The cross product of and given by is as shown in Fig. 4. In the figure, a -edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.
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 be the cross product of GSs and If and are respective m-PFGSs of and then is an m-PFGS of By the Definition 12 of cross product, and are m-polar fuzzy sets of and , respectively, for all i. So remaining task is to prove that is an m-polar fuzzy relation on for all i. For this, proceed as follows:
If and , then
This holds for every Hence is an m-polar fuzzy relation on , for all i, which completes the proof.
We now define lexicographic product of m-polar fuzzy graph structures.
Definition 15
Let and be two m-PFGSs. Then the lexicographic product of and is given by
where the mappings and (for ) are respectively defined by
and
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 and shown in the Figs. 1 and 2, respectively. The lexicographic product of and given by is as shown in Fig. 5. In the figure, a -edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.
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 be the lexicographic product of GSs and If and are respective m-PFGSs of and then is an m-PFGS of By the Definition 15 of lexicographic product, and are m-polar fuzzy sets of and , respectively, for all i. Now, remaining task is to prove that is an m-polar fuzzy relation on for all i. For this, we discuss two cases as follows:
Case 1. When and
Case 2. When and ,
This holds for every Hence is an m-polar fuzzy relation on , for all i, which completes the proof.
We now give definition of strong product of m-polar fuzzy graph structures.
Definition 18
Let and be two m-PFGSs. Then the strong product of and is given by
where the mappings and (for ) are respectively defined by
and
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 and shown in the Figs. 1 and 2, respectively. The strong product of and given by is as shown in Fig. 6. In the figure, a -edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.
Fig. 6.

Strong product of two 4-PFGSs
We formulate strong product of and as a proposition.
Proposition 20
Strong product of two m-polar fuzzy graph structures is anm-polar fuzzy graph structure.
Proof
Let GS be the strong product of GSs and Let and be respective m-PFGSs of and Then is an m-PFGS of By Definition 18 of strong product, is an m-polar fuzzy set of and is an m-polar fuzzy set of for all i. So the remaining task is to prove that is an m-polar fuzzy relation on for all i. For this, some cases are discussed, as follows:
Case 1. When and
Case 2. When
Case 3. When and ,
All three cases hold for every This completes the proof.
We define the notion of composition of two m-polar fuzzy graph structures.
Definition 21
Let and be two m-PFGSs. Then composition of and is given by
where the mappings and (for ) are respectively defined by
and
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 and shown in the Fig. 1 and The composition of and given by is as shown in Fig. 7. In the figure, a -edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.
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 be the composition of GSs and Let and be respective m-PFGSs of and Then is an m-PFGS of By Definition 21 of composition, is an m-polar fuzzy set of and is an m-polar fuzzy set of for all i. Therefore the remaining task is to show that is an m-polar fuzzy relation on for all i. For this, consider the following cases:
Case 1. When and
Case 2. When
Case 3. When and , such that ,
All three cases hold for every This completes the proof.□
We now introduce the concept of union of two m-polar fuzzy graph structures.
Definition 24
Let and be two m-PFGSs. Then union of and is given by
where the mappings and (for ) are respectively defined by
and
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 and shown in the Figs. 1 and 2, respectively. The union of and given by is as shown in Fig. 8. In the figure, a -edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.
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 be the union of GSs and Let and be respective m-PFGSs of and Then is an m-PFGS of From the Definition 24 of union, is an m-polar fuzzy set of and is an m-polar fuzzy set of for all i. So the remaining task is to show that is an m-polar fuzzy relation on for all i. For this, consider following cases:
Case 1. When , then there are three possibilities (i) (ii) (ii) . So for all
Case 2. When , then there are three possibilities (i) (ii) (ii) . So for all
Case 3. When , then . So
All three cases hold for every Hence is an m-polar fuzzy relation on for all i. This completes the proof.
Theorem 27
If GS is the union of GSs and Then everym-PFGS ofis the union of an m-PFGS ofand an m-PFGS of
Proof
Observe that , and and are m-polar fuzzy sets on and , respectively, for if for every j, we define and as:
For , is an m-polar fuzzy relation on , since
Therefore, is a m-PFGS of for and m-PFGS is union of m-PFGS and m-PFGS . Hence every m-PFGS of is the union of some m-PFGSs of for □
Finally, we study the concept of join of two m-polar fuzzy graph structures.
Definition 28
Let and be two m-PFGSs such that . Let and . Then join of and is given by
where the mappings and (for ) are respectively defined by
and
where j varies from 1 to m.
Example 29
Consider the 4-PFGSs and shown in the Figs. 1 and 2, respectively. The join of and given by is as shown in Fig. 9. In the figure, a -edge can be identified by the subscript “i” with the corresponding degrees of memberships of edge.
Fig. 9.

Join of two m-PFGSs
Proposition 30
Let GS be the join of GSs and Let andbe respective m-PFGSs of and Thenis an m-PFGS of
Proof
From the Definition 28 of Join, is an m-polar fuzzy set of and is an m-polar fuzzy set of for all i. So the remaining task is to show that is an m-polar fuzzy relation on for all i. For this, consider following cases:
Case 1. When , then . So
Case 2. When , then . So
Case 3. When , then . So
Hence is an m-polar fuzzy relation on in all three cases. All cases hold for every This completes the proof.
Theorem 31
If GS is the join of GSs and Then every strongm-PFGS ofis the join of a strong m-PFGS of and a strongm-PFGS of □
Proof
Let be a strong m-PFGS of . Define and for every j, as follows:
Observe that and are m-polar fuzzy sets on and , respectively, for . For , is an m-polar fuzzy relation on , so is a strong m-PFGS of since
for all . Moreover, and , since for all and for all Therefore m-PFGS is join of m-PFGS and m-PFGS . Hence a strong m-PFGS of is the join of a strong m-PFGSs of and a strong m-PFGSs of . 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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