Advances in Fuzzy Systems

Volume 2017, Article ID 6162753, 17 pages

https://doi.org/10.1155/2017/6162753

## A New Type-2 Soft Set: Type-2 Soft Graphs and Their Applications

^{1}School of Mathematics and Information Sciences, Guangzhou University, Guangzhou 510000, China^{2}Department of Mathematics, Islamabad Model College for Girls, F-6/2, Islamabad, Pakistan^{3}Guangzhou Vocational College of Science and Technology, Guangzhou 510550, China

Correspondence should be addressed to Muhammad Irfan Ali; moc.oohay@31ilanafrim and Bing-Yuan Cao; moc.361@ygniboac

Received 7 February 2017; Accepted 15 June 2017; Published 18 October 2017

Academic Editor: Katsuhiro Honda

Copyright © 2017 Khizar Hayat et al. 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.

#### Abstract

The correspondence between a vertex and its neighbors has an essential role in the structure of a graph. Type-2 soft sets are also based on the correspondence of primary parameters and underlying parameters. In this study, we present an application of type-2 soft sets in graph theory. We introduce vertex-neighbors based type-2 soft sets over (set of all vertices of a graph) and (set of all edges of a graph). Moreover, we introduce some type-2 soft operations in graphs by presenting several examples to demonstrate these new concepts. Finally, we describe an application of type-2 soft graphs in communication networks and present procedure as an algorithm.

#### 1. Preliminaries and Introduction

A graph consists of a nonempty set of objects , called vertices, and a set of pair of the element of , called edges. Two vertices and are adjacent if . A simple graph is an unweighted, undirected graph containing no multiple edges or graph loops. A graph is said to be a subgraph of if and The neighborhoods of a vertex in a graph are the set of all the vertices adjacent to including itself. The neighbors of a vertex in a graph are the set of all the vertices adjacent to excluding itself. The eccentricity of the vertex is the maximum distance from to any vertex. The radius of a graph is the minimum eccentricity of any vertex

Graph theory is rapidly moving into the mainstream of mathematics, mainly because of its applications in diverse fields which include electrical engineering (communications networks and coding theory), computer science (algorithms and computations), and operations research (scheduling). In many cases, it is important to deal with uncertainties using fuzzy sets and logics. The fuzzy graph is a weighted graph which gives a normalized relational strength over a fuzzy subset of a set [1]. Kauffman [2] gave the fundamental definition of a fuzzy graph on the basis of Zadeh’s fuzzy relations [3]. Rosenfeld [1] introduced another elaborated definition of fuzzy graphs. Subsequently, Bhattacharya [4] gave some useful results on fuzzy graphs and Mordeson and Nair [5] investigated some operations on fuzzy graph theory. In recent decades, fuzzy graphs were studied by many researchers. Akram et al. extended the concept of fuzzy graphs to bipolar fuzzy graphs [6], intuitionistic fuzzy hypergraphs [7], intuitionistic fuzzy graphs structures [8], and interval valued fuzzy graph [9]. Samanta et al. [10–12] studied vague graphs, fuzzy planar graphs, completeness and regularity of generalized fuzzy graph, and irregular bipolar fuzzy graphs.

Soft set theory [13], firstly initiated by Molodtsov, is a new mathematical tool for dealing with uncertainties. Indeed, Molodtsov’s approach shows the applicability of soft sets to several fields and provides some fundamental results subsequently augmented by works like Maji et al. [14] and Aktaş and Çağman [15], among others. Ali et al. [16] presented some new operations in soft set theory and based on the analysis of several operations on soft sets Sezgin and Atagün [17] studied the theoretical aspect of the soft set theory.

Let be a set of parameters that can have an arbitrary nature (numbers, functions, sets of words, etc.). Let be a universal set and the power set of is denoted by The formal definition of soft is defined as follows.

*Definition 1 (see [14]). *A pair is called a soft set over , where is a mapping given by

We refer to Molodtsov’s soft sets as type-1 soft sets (briefly T1SS). Note that the set of all T1SS over will be denoted by We refer to [13, 16, 18] for basic notions of soft set theory.

The applications of the soft set are progressing rapidly and many researchers are focusing on real and practical problems. Interestingly, some progress of soft sets has developed with graph theory. Ali et al. [19] introduced a new representation of graphs based on neighborhoods and soft sets. Akram and Nawaz presented the concepts of soft graphs [20, 21] and fuzzy soft graphs [22]. Let be a simple graph; let be any nonempty set. Let be an arbitrary relation from to A mapping from to is denoted as and defined as and a mapping from to is denoted as and defined as . Then is a T1SS over and is a T1SS over . The notion of a soft graph is defined as follows.

*Definition 2 (see [21]). *A -tuple is called a soft graph if it satisfies the following conditions:(i) is a simple graph.(ii) is a nonempty set of parameters.(iii) is a T1SS over .(iv) is a T1SS over .(v), for all , represents a subgraph of .The soft graph can also be written as .

In the rest of the paper, soft graph will be written as type-1 soft graph (briefly, T1SG). Some operations of T1SG are defined as follows.

*Definition 3 (see [20]). *Let and be two T1SG of Then is a type-1 soft subgraph of if(i),(ii)for each , is a subgraph of .

*Definition 4 (see [20]). *Let and be two T1SG of The extended union of and , denoted by , where , is defined, , asIt can be written as

*Definition 5 (see [20]). *Let and be two T1SG in The restricted intersection of and , denoted by , where , is defined, , as ,

It can be written as

*Definition 6 (see [20]). *Let and be two T1SG in The operation of and , denoted by , where , is defined, , as ,

*Definition 7 (see [20]). *Let and be two T1SG in . The operation of and , denoted by , where , is defined, , as ,

*Definition 8 (see [20]). *Let and be two T1SG of and , respectively, such that The* Cartesian product* of and is denoted and defined by , , , where and are the subgroups of and , respectively.

Note that denotes the Cartesian product of two crisp subgraphs. That is, .

For basic definitions of graph and soft graph, see [20, 21, 23–25].

In order to deal with associations between parameters, Chatterjeea et al. [26] propose the concept of type-2 soft set which is a generalization of Molodtsov’s soft set. It involves parameterization over an already parameterized set and hence has more freedom and efficiency compared to usual soft sets (termed as type-1 soft sets) in handling impreciseness. In fact, type-2 soft sets are a new approach to managing uncertainty. Interestingly, Chatterjeea et al. [26] investigate some basic operations on type-2 soft sets and use the model of type-2 soft sets in decision making problems. Recently, distance, entropy, and similarity measures of type-2 soft sets are introduced by Chatterjeea et al. [27].

Let be universe set and let be the set of parameters.

*Definition 9 (see [26]). *Let be a soft universe and let be the collection of all T1SS over . Then a mapping , is called a type-2 soft set (briefly T2SS) over and it is denoted by . In this case, corresponding to each parameter , is a T1SS. Thus, for each , there exists a T1SS, such that , where and . In this case, we refer to the parameter set as the “primary set of parameters,” while the set of parameters is known as the “underlying set of parameters.”

In this study, we present an application of type-2 soft set in graph theory. We introduce vertex-neighbors based type-2 soft sets over (set of all vertices of a graph) and (set of all edges of a graph). Moreover, we introduce some type-2 soft operations in graphs by presenting several examples to demonstrate these new concepts. We describe an application of type-2 soft graphs in communication networks and present procedure as an algorithm.

#### 2. Type-2 Soft Graphs

The behavior and the selections of initial parameters and associated parameterized sets corresponding to initial parameters make the study of type-2 soft sets very interesting. Motivated by this, we study type-2 soft sets in graph theory and related properties. In a graph, every vertex regarding its location consists of a special relationship with its neighbors. Suppose that a company makes an analysis on the flow of transportation in two cities “” and “” of a country. We consider edge (line segment (link) between locations, that, is road) and vertex (location on the transportation network of interest (node), that is, town and road intersections). The company must make a survey on the flow of transportation from neighbor cities to control transportation in “” and “” and obtain a graphical representation to analyze the flow of transportation. The vertex-neighbors correspondence has an essential role in the structure of a graph. The type-2 soft set is also based on the correspondence of initial parameters and underlying parameters. The set of all neighbors of an element is defined as follows.

*Definition 10. *Let be a simple graph. The set of neighbors of an element is denoted by and defined by

Let be a simple graph and . Then Let a subset of be an arbitrary relation from to . The set of all T1SS over is denoted by and the set of all T1SS over is denoted by

*Definition 11. *Let be a simple graph; let and be the collection of all T1SS over . Then a mapping is called a T2SS over and it is denoted by . In this case, corresponding to each vertex , is a T1SS such that , where can be defined as , for all , and is the set of all neighbors of . This T2SS is also called a vertex-neighbors induced type-2 soft set (briefly, VN-type-2 soft set) over .

*Definition 12. *Let be a simple graph; let and be the collection of all T1SS over . Let be a VN-type-2 soft set over . Then a mapping is called a T2SS over and it is denoted by . In this case, corresponding to each vertex , is a T1SS such that , where can be defined as , for all , and is the set of all neighbors of . This T2SS is also called a VN-type-2 soft set over .

As it is mentioned in the above definitions, and are T1SS over and over , respectively. If represents a subgraph of , then by the definition of T1SG will be a T1SG of . This T1SG can also be represented by

##### 2.1. Type-2 Soft Graph

A 5-tuple is called a type-2 soft graph (briefly, T2SG) if it satisfies the following conditions:(i) is a simple graph.(ii) is a nonempty set of parameters.(iii) is a VN-type-2 soft set over .(iv) is a VN-type-2 soft set over .(v)T1SS corresponding to , represents a type-1 soft graph.

A T2SG can also be represented by , where such that , The set of all T2SG of is denoted by .

Although the above representation of graphs provides a new framework for the applications of graph theory, it is restricted to the information from neighbors. In this regard, a likeness between parameterized vertices appears in some cases such as distinction between T2SG and is limited toward respective T1SG. However, T2SG is more general and hence a more efficient approach in graph theory as compared to T1SG.

*Definition 13. *A T2SG is said to be vertex-induced if for all .

*Definition 14. *A T2SG is said to be edge-induced if for all .

*Example 15. *Consider a graph as shown in Figure 1.

Let . Then , . Let and be two T2SS over and , respectively, such that and for all . Let , and , . ThenFigure 2 shows the respective T1SG corresponding to and , respectively. Hence, is a T2SG of . It is also called VN-type-2 soft graph.