Volume 2022, Issue 1 8597666
Research Article
Open Access

[Retracted] On Development of Neutrosophic Cubic Graphs with Applications in Decision Sciences

Fazeelat Sultana

Fazeelat Sultana

Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan hu.edu.pk

Army Burn Hall College for Girls, Abbottabad, Pakistan

Search for more papers by this author
Muhammad Gulistan

Muhammad Gulistan

Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan hu.edu.pk

Search for more papers by this author
Peide Liu

Peide Liu

School of Management Science and Engineering, Shandong University of Finance and Economics, China sdufe.edu.cn

Search for more papers by this author
Mumtaz Ali

Mumtaz Ali

Deakin-SWU Joint Research Centre on Big Data, School of Information Technology Deakin University, VIC 3125, Australia

Search for more papers by this author
Zahid Khan

Corresponding Author

Zahid Khan

Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan hu.edu.pk

Search for more papers by this author
Mohammed M. Al-Shamiri

Mohammed M. Al-Shamiri

Department of Mathematics, Faculty of Science and Arts, Muhayl Asser, King Khalid University, Saudi Arabia kku.edu.sa

Department of Mathematics and Computer, Faculty of Science, IBB University, IBB, Yemen ibb-univ.net

Search for more papers by this author
Muhammad Azhar

Muhammad Azhar

Department of Mathematics, Abbottabad University of Science & Technology, Abbottabad, Pakistan aust.edu.pk

Search for more papers by this author
First published: 22 March 2022
Citations: 1
Academic Editor: Jia-Bao Liu

Abstract

In this study, the neutrosophic cubic graphs are further developed. We discussed and explored the open and the closed neighborhood for any vertex in neutrosophic cubic graphs, regular and totally regular neutrosophic cubic graphs, complete neutrosophic cubic graphs, balanced and strictly balanced neutrosophic cubic graphs, irregular and totally irregular neutrosophic cubic graphs, complement of a neutrosophic cubic graph, neighborly irregular and neighborly totally irregular neutrosophic cubic graphs, and highly irregular neutrosophic cubic graphs. It has been demonstrated that the proposed neutrosophic cubic graphs are associated with specific conditions. The comparison study of the proposed graphs with the existing cubic graphs has been carried out. Eventually, decision-making approaches for handling daily life problems such as effects of different factors on the neighboring countries of Pakistan and selection of a house based on the notions of proposed graphs are presented.

1. Introduction

A human being has a higher position among all the creatures due to his ability to analyze and make decisions. The decisions are made by carefully scrutinizing the problem based on the experience and the current situation. In the past, this used to be a mental activity with its successful execution. With the advancement of science and technology, it is now possible to use some modern techniques to address this problem better. These methodologies rely on traditional knowledge virtually. The ability of humans has been mimicked out effectively by making use of artificial intelligence. Some artificial intelligence techniques have been used successfully to chalk out good decisions. In this approach, various instruments related to decision-making are used. There is a well-known approach called graph theory. Graph theory is the systematic and logical way to analyze and model many applications related to science and other social issues. Graph theory is an essential tool and has played a significant role in developing graph algorithms in computer-related applications. These algorithms are quite helpful in solving theoretical aspects of the problems. These techniques help in solving geometry, algebra, number theory, topology, and many other fields. But many issues are not practically solvable due to the crisp nature of the classical sets. So in 1965, Zadeh [1] introduced the notion of the fuzzy subset of a set. Many other extensions of fuzzy sets have been developed so far like interval-valued fuzzy sets [2] by Zadeh, intuitionistic fuzzy sets [3, 4] by Atanassov, and cubic sets [5, 6] by Jun et al. In [7], Akram et al. developed cubic KU-subalgebras. Smarandache extended the concept of Atanassov and gave the idea of neutrosophic sets [8, 9], and interval neutrosophic sets were introduced by Wang et al. [10]. Jun et al. gave the idea of a neutrosophic cubic set [11]. Rosenfeld [12] developed the fuzzy graphs in 1975. Bhattacharya [13] had started contributing in fuzzy graphs in 1987. Arya and Hazarika [14] developed functions with closed fuzzy graphs. Bhattacharya and Suraweera [15] had developed an algorithm to compute the supremum of max-min powers and a property of fuzzy graphs. Bhutani [16] studied automorphisms of fuzzy graphs. Cerruti [17] used graphs and fuzzy graphs in fuzzy information and decision processes. Chen [18] discuss matrix representations of fuzzy graphs. Crain [19] studied characterization of fuzzy interval graphs. After that, many others contributed to fuzzy graph’s theory like Mordeson and Nair’s contribution [20], Gani and Radha [21], Rashmanlou and Pal [22], Nandhini and Nandhini [23], Elmoasry et al. [24], and Akram et al. [2527]. Other contributions are from Gani and Latha [28], Poulik et al. [2932], Borzooei and Rashmanlou [33], Buckley [34], Rashmanlou and Pal [35], Mishra et al. [36], Pal et al. [37], Pramanik et al. [38, 39], Shannon and Atanassov [40], Parvathi et al. [41, 42], and Sahoo and Pal [43]. Akram [44, 45] initiated the concept of bipolar fuzzy graphs. Many others contributed on bipolar fuzzy graphs, like Rashmanlou et al. [46], Akram and Karunabigai [47], and Samanta and Pal [48]. Graphs in terms of neutrosophic’s set have been studied by Huang et al. [49], Naz et al. [50], Dey et al. [51], Broumi et al. [52], and Karaaslan and Dawaz [53]. Zuo et al. [54] discussed picture fuzzy graphs. Kandasamy et al. and Smarandache [55, 56] developed neutrosophic graphs for the first time. Broumi et al. [5761] discussed different versions of neutrosophic graphs. More development on the neutrosophic graphs can be seen in [50, 6264]. After reading the extensive literature at neutrosophic graphs, recently, Gulistan et al. [65] discussed the cubic graphs with the application, neutrosophic graphs, and presented the idea of neutrosophic cubic graphs and their structures in their work [66, 67].

To further extend the work of Gulistan et al. [66, 67], in this paper we developed different types of neutrosophic cubic graphs including balanced, strictly balanced, complete, regular, totally regular, and irregular neutrosophic cubic graphs and complement of neutrosophic cubic graphs. Also, we established an open and close neighborhood of a vertex for neutrosophic cubic graphs and their application to the art of decision-making. The properties related to these newly suggested neutrosophic cubic graphs are also shown and how they are correlated. The arrangements of the paper are as follows: Section 2 is a review of basic concepts with their properties of neutrosophic cubic graphs. Section 3 describes different types of neutrosophic cubic graphs with examples. We also provide some results related to different types of neutrosophic cubic graphs. We present applications and a decision-making technique in Section 4. In Section 5, we provide a comparative analysis. Conclusions and suggested future work are presented in Section 6.

2. Preliminaries

This section consists of two parts: notations and predefined definitions.

2.1. Notations

Some notations with their descriptions are given in Table 1.

Table 1. Notations and their descriptions.
S.no Notation Description
1 G An arbitrary graph
2 A Vertex set for G graph
3 B Edge set for G graph
4 FG Fuzzy graph
5 θ Degree
6 Density
7 α Membership function for vertex set in FG
8 β Membership function for edge set in FG
9 N Neighborhood
10
Represents an interval [al, ar] with 0 ≤ alar ≤ 1
11
Neutrosophic cubic set
12
Neutrosophic cubic graph
13 Φ Membership function for vertex set in
14 Ψ Membership function for edge set in .
15
Fuzzy neighborhood of a vertex g in FG
16 ncg(g) Open neighborhood for any vertex g in
17 ncg[g] Closed neighborhood for any vertex g in

2.2. Predefined Definitions

In this subsection, we added some important definitions which are directly used in our work.

Definition 1 (see [66].)A neutrosophic cubic graph for a crisp graph G = (A, B) is a pair with

(1)
representing neutrosophic cubic vertex set A, and
(2)
shows neutrosophic cubic edge set B such that
(3)
for every vertex g1, g2A and edge g1g2B.

3. Different Types of Neutrosophic Cubic Graphs

This section contains definitions for different neutrosophic cubic graphs with a good discussion on some of their related results.

3.1. The Open and the Closed Neighborhood for Any Vertex in

In this subsection, we present the idea of open neighborhood ncg(g), degree of open neighborhood θ(ncg(g)), and closed neighborhood degree of neutrosophic cubic graph .

Definition 2. An open neighborhood ncg(g) for any vertex g in is given by

(4)

where

(5)

It consists of membership functions for all vertices adjacent to g, excluding s.

Definition 3. The degree of open neighborhood θ(ncg(g)) for any vertex g in is defined by

(6)

Example 4. Let G = (A, B) with vertices A = {g1, g2, g3} and edges B = {g1g2, g2g3, g1g3}. Also, such that

(7)
then clearly, is a neutrosophic cubic graph as shown in Figure 1.

Details are in the caption following the image
Represents a neutrosophic cubic graph.
θ(ncg(g)) for each element gA is given by
(8)

Definition 5. A closed neighborhood degree for a vertex g in is given by

(9)

It consists of the sum of membership functions of all vertices adjacent to x and membership function of x.

Example 6. Consider Example 4, closed neighborhood degree θ(ncg[g]) for each element gA in is given by

(10)
similarly,
(11)
also, we have
(12)

3.2. Regular and Totally Regular Neutrosophic Cubic Graphs

In this subsection, we present the idea of regular and totally regular neutrosophic cubic graphs based on the open neighborhood degree and closed neighborhood degree.

Definition 7. If every vertex in has the same open neighborhood degree n, i.e., if θ(ncg(g)) = n, for all gA, then is called an n regular neutrosophic cubic graph.

Definition 8. If closed neighborhood degree is the same for all vertices in , i.e., if θ(ncg[g]) = m, for all gA, then is called an m-totally regular neutrosophic cubic graph.

Example 9. Consider Example 4; here, is a totally regular but not a regular neutrosophic cubic graph.

Example 10. Consider for any graph G = (A, B) with A = {v1, v2, v3}, B = {v1v2, v2v3, v1v3} and let

(13)
then the neutrosophic cubic open neighborhood degree of each vertex is given by
(14)
similarly, the neutrosophic cubic closed neighborhood degree of each vertex is given by
(15)

As θ(ncg(v1)) = θ(ncg(v2)) = θ(ncg(v3)) also θ(ncg[v1]) = θ(ncg[v2]) = θ(ncg[v3]). Hence, is regular, also totally regular neutrosophic cubic graph, as shown in Figure 2.

Example 11. Let be a neutrosophic cubic graph of G = (A, B) with A = {v1, v2, v3}, B = {v1v2, v2v3, v1v3} such that Γ and Λ are given by Tables 2 and 3.

Details are in the caption following the image
Represents a regular and totally regular neutrosophic cubic graph.
Table 2. A neutrosophic cubic membership for vertices.
Γ = A
αT
αI
αF
v1 [.3.5] .4 [.1.3] .2 [.5.6] .5
v2 [.2.4] .3 [:1.4] .3 [.6.7] .7
v3 [.1.3] .2 [:2.3] .7 [.7.8] .6
Table 3. A neutrosophic cubic membership for edges.
Λ = B
βT
βI
βF
v1v2 [.2.4] .4 [.1.3] .2 [.3.5] .6
v2v3 [.1.3] .3 [.1.3] .3 [.4.6] .5
Then,
(16)
(17)

Since θ(ncg(v1) ≠ θ(ncg(v2)) although θ(ncg(v1)) = θ(ncg(v3)), so is not regular as well as totally regular as shown in Figure 3.

Theorem 12. Let be a neutrosophic cubic graph of G, with Γ showing for vertex set A and Λ is for edge set B. Then,

(18)
is a constant if and only if we have equivalence in the following:
  • (I)

    is regular

  • (II)

    is totally regular

Proof. Suppose

(19)
for all gA, where k is some constant, then
(20)
for all gA and for some constants t1, t2, t, i1, i2, i, f1, f2, f.

(I)⇒(II) Let be a regular, then θ(ncg(g)) = n for all gA. So,

(21)

Hence, for all gA, we have

(22)

Thus,

(23)
i.e.,
(24)
a constant number, for all gA. Thus, is totally regular.

(II)⇒(I) Suppose that is totally regular. Then, for all gA,

(25)
i.e.,
(26)

Let

(27)
where m1t, m2t, mt, m1i, m2i, mi, m1f, m2f, mf are all constants. Also, given that for every gA,
(28)
where k is a constant; also, let
(29)

Then,

(30)
for all gA. Hence, for all gA,
(31)
which a constant number. Thus, is regular. So, (I) and (II) have equivalence. Conversely, if is totally regular, then θ(ncg[g]) = m (a constant) for all gA; also, is regular. So, θ(ncg(g)) = n say for every gA. Hence, for every gA,
(32)

a constant no for every gA. Hence,

(33)

for all gA is a constant function.

Theorem 13. Consider as a neutrosophic cubic graph with crisp graph G of an odd cycle. Then, is regular if and only if Λ is a constant function.

Proof. Let

(34)

be a constant function for all xyB. Then,

(35)

Now,

(36)

Since G is an odd cycle, is regular. Conversely, suppose that is an n- regular, where

(37)

Let e1, e2, e3, ⋯, e2n+1 be edges of in that order. Let

(38)
and so on. Therefore,
(39)

This implies

(40)

So, if e1 and e2n+1 are incident at a vertex v1, then

(41)

Hence,

(42)
and so, k1 = n1t/2, which shows that is a constant function. Similarly, let
(43)
and so on. Therefore,
(44)

Thus, So, if e2 and e2n are incident at a vertex v2, then

(45)

Hence,

(46)

and so, k2 = n2t/2, which shows that is a constant function.

Similar results hold for membership functions . This shows that

(47)

is a constant function.

Details are in the caption following the image
Shows a neutrosophic cubic graph, which is not regular as well as totally regular.

3.3. Complete Neutrosophic Cubic Graphs

In this subsection, we present complete neutrosophic cubic graph .

Definition 14. Consider be a neutrosophic cubic graph for any arbitrary graph G = (A, B). Then, is complete if

(48)
for all vertices v1, v2A and for all edges v1v2B.

Example 15. Consider for a graph G = (A, B) with A = {v1, v2, v3}, B = {v1v2, v2v3, v1v3} and let

(49)
(50)
then
(51)
(52)

Also,

(53)
similar holds for other edges. Hence, is a complete neutrosophic cubic graph, as shown in Figure 4.

Definition 16. Let be a neutrosophic cubic graph for some graph G = (V, E). The density of is defined as

(54)
where
(55)

Example 17. Consider for a graph G = (A, B), with vertex set A = {v1, v2, v3} and edge set B = {v1v2, v2v3, v1v3}. Also, let Γ and Λ be neutrosophic membership functions for vertices and edges, respectively, shown in Tables 4 and 5.

Details are in the caption following the image
Represents a complete neutrosophic cubic graph.
Table 4. Neutrosophic membership functions for vertices.
Γ = A
αT
αI
αF
v1 [.1.5] .6 [.5.7] 1 [.6.7] .9
v2 [.2.6] .5 [.6.8] .8 [.7.8] .8
v3 [.3.7] .8 [.7.8] .7 [.5.6] .7
Table 5. Neutrosophic membership functions for edges.
Λ = B
βT
βI
βF
v1v2 [.1.2] .5 [.4.5] .4 [.5.6] .2
v2v3 [.2.4] .2 [.1.3] .5 [.4.5] .3
v1v3 [.1.2] .5 [.1.3] .6 [.4.6] .2
Since
(56)
Also,
(57)
So,
(58)
Hence, is given by
(59)

3.4. Balanced and Strictly Balanced Neutrosophic Cubic Graphs

In this subsection, we use the density function ℘ to discuss the idea of balanced and strictly balanced neutrosophic cubic graph .

Definition 18. is balanced if for all subgraphs H of .

Definition 19. is strictly balanced if for all nonempty subgraphs H of .

Example 20. Consider as given in Example 17. Let H1 = {a, b}, H2 = {a, c}, H3 = {b, c}. Then,

(60)
also,
(61)

Hence,

(62)
as for all nonempty subgraphs H of . Hence, is not balanced.

Remark 21. All regular neutrosophic cubic graphs are not necessarily balanced.

3.5. Irregular and Totally Irregular Neutrosophic Cubic Graphs

In this subsection, we use the neighborhood degrees to discuss the idea of irregular and totally irregular neutrosophic cubic graph .

Definition 22. If there is at least one vertex in adjacent to vertices having different open neighborhood degrees, then is called irregular, i.e., if θ(ncg(v)) ≠ n for all vA.

Example 23. Consider for some graph G = (A, B), with A = {v1, v2, v3, v4} and

(63)
and let
(64)
(65)
then
(66)
hence,
(67)

Hence, is irregular as shown in Figure 5.

Definition 24. A connected is totally irregular, if at least one vertex is adjacent to the vertices having different closed neighborhood degrees.

Example 25. Consider for G = (A, B), with

(68)

and let

(69)
(70)

Then,

(71)

Details are in the caption following the image
Represents irregular neutrosophic cubic graph.

Clearly, is totally irregular as in Figure 6.

Details are in the caption following the image
Represents totally irregular neutrosophic cubic graph.

3.6. Complement of a Neutrosophic Cubic Graph

Complement of a neutrosophic cubic graph is a very important concept we discuss here.

Definition 26. The complement of is a neutrosophic cubic graph where

(72)
since
(73)
or for truth membership functions, we have
(74)

similarly, for indeterminate membership functions, we have

(75)
also, for falsity membership functions, we have similar results.

Proposition 27. For self-complementary , we have

(76)

Proof. Given is self-complementary, so ; also, by definition of a self-complementary neutrosophic cubic graph, we have

(77)

Dividing both sides of equation (77) by min{Φ(x), Φ(y)},

(78)
we get
(79)

Hence,

(80)
so
(81)

Proposition 28. Let be strictly balanced, let be its complement, then

Proof. Let be strictly balanced; let be its complement. Let H be a nonempty subgraph of Since is strictly balanced, for every subset and for any x, yA. In , we have , and so, for truth membership functions, we have

(82)

Dividing equation (82) by , we get

(83)

Hence,

(84)

Multiplying both sides by 2,we get

(85)

Hence, . Similarly for right end point of interval in truth valued membership functions, we have . Similar results hold for the rest of membership functions. Hence,

(86)

This completes the proof.

3.7. Neighborly Irregular and Neighborly Totally Irregular Neutrosophic Cubic Graphs

In this subsection, we use the neighborhood degrees to discuss the idea of neighborly irregular and neighborly totally irregular neutrosophic cubic graphs.

Definition 29. A connected is neighborly irregular if every two adjacent vertices in have different closed neighborhood degrees.

Example 30. Consider for any G = (A, B) with A = {a, b, c, d} and B = {ab, bc, cd, da} and let neutrosophic cubic membership functions for vertices and edges be represented in Tables 6 and 7, respectively.

Table 6. Neutrosophic membership functions for vertices.
Γ = A
αT
αI
αF
a [.2.6] .3 [.1.3] .2 [.5.7] .5
b [.3.7] .2 [.2.4] .3 [.4.5] .6
c [.4.8] .3 [.3.5] .4 [.6.7] .4
d [.5.7] .4 [.1.2] .3 [.7.8] .7
Table 7. Neutrosophic membership functions for edges.
Λ = B
βT
βI
βF
ab [.1.4] .2 [.1.2] .1 [.3.5] .5
bc [.2.5] .1 [.2.3] .2 [.4.5] .3
cd [.3.6] .3 [.1.2] .3 [.5.6] .4
da [.2.5] .2 [.1.2] .2 [.4.5] .5

Then, clearly is neighborly irregular, as shown in Figure 7.

Definition 31. is said to be neighborly totally irregular if every two adjacent vertices of have different closed neighborhood degrees.

Example 32. Consider for any G = (A, B) with A = {v1, v2, v3, v4} and B = {v1v2, v2v3, v3v4, v1v4}. Also, let

(87)
(88)

Details are in the caption following the image
Represents neighborly irregular neutrosophic cubic graph.

Then, clearly is neighborly totally irregular, as shown in Figure 8.

Details are in the caption following the image
Represents neighborly totally irregular neutrosophic cubic graph.

3.8. Highly Irregular Neutrosophic Cubic Graphs

In this subsection, we use the neighborhood degrees to discuss the idea of highly irregular neutrosophic cubic graphs.

Definition 33. Consider a connected ; then, is highly irregular if every vertex in is adjacent to vertices with different neighborhood degrees.

Example 34. Consider for a graph G = (A, B) with A = {v1, v2, v3, v4, v5, v6} and B = {v1v2, v2v3, v2v6, v3v4, v3v5, v4v5, v1v5} and let

(89)

By routine computations, we have

(90)

Clearly, as shown in Figure 9 is highly irregular.

Theorem 35. is highly irregular and neighborly irregular if and only if open neighborhood degrees for all vertices of are different.

Proof. Suppose has n vertices v1, v2, ⋯, vn. Also, let be highly irregular and neighborly irregular.

Claim 1. The open neighborhood degrees for all vertices in are different. Let

(91)

for all i = 1, 2, 3, ⋯, n. Let the adjacent vertices of v1 be v2, v3, ⋯, vn with open neighborhood degrees:

(92)
for all i = 2, 3, ⋯, n, respectively. Then, as is highly irregular, we have
(93)
for all i = 2, 3, ⋯, n. Similar holds for indeterminacy and falsity membership functions. Also, is neighborly irregular, so we have
(94)
Similar holds for indeterminacy and falsity membership functions. Hence, open neighborhood degrees of all vertices of are different.

Claim 2. is highly irregular and neighborly irregular. Let

(95)
for all i = 1, 2, 3, ⋯, n are degrees for all vertices of . Given open neighborhood degrees of all vertices of are different, so
(96)
for all i = 1, 2, 3, ⋯, n. Similar holds for indeterminacy and falsity membership functions. So, any two vertices in have different open neighborhood degrees, and for every vertex, adjacent vertices have different open neighborhood degrees, which proves the result.

Remark 36. A complete may not be neighborly irregular.

Example 37. Let for any G = (A, B), with A = {v1, v2, v3} and B = {v1v2, v2v3, v1v3} such that

(97)
(98)

By simple computation, we get

(99)

Details are in the caption following the image
Represents highly irregular neutrosophic cubic graph.

Here, θ(ncg(v2)) = θ(ncg(v3)), so the neighborhood degree is not different. Hence, is not neighborly irregular; also, we have similar holds for all vertices and edges. So, is complete. Hence, a complete may not be neighborly irregular as shown in Figure 10.

Theorem 38. If is neighborly irregular and

(100)
for all xA is a constant function, then it is neighborly totally irregular.

Proof. Assume that is a neighborly irregular. Then, open neighborhood degrees of every two adjacent vertices are different. Let vi, vjA be adjacent vertices with different open neighborhood degrees. Then, θ(ncg(vi)) ≠ θ(ncg(vj)) for all ij; let θ(ncg(vi)) = d1&θ(ncg(vj)) = d2 then d1d2. Also, as

(101)
is constant for all xA. Hence, Φ(vi) = Φ(vj) = k; suppose that is not neighborly totally irregular, then
(102)
for some ij but θ(ncg[vi]) = θ(ncg(vi)) + Φ(vi) and θ(ncg[vj]) = θ(ncg(vj)) + Φ(vj) using these values in equation (102), we get
(103)
as cancellation law holds in [0, 1], which contradicts, as
(104)

Hence,

(105)
so is neighborly totally irregular. This proves the result.

Theorem 39. If is neighborly totally irregular and

(106)
is a constant function, then it is neighborly irregular.

Proof. Assume that is a neighborly totally irregular. Then, closed neighborhood degrees of every two adjacent vertices are distinct. Let vi, vjA be adjacent vertices with distinct closed neighborhood degrees. Then, for all ij,

(107)
let
(108)
then f1f2. Also, as
(109)
suppose that is not neighborly irregular, then
(110)
say, for some ij but
(111)
(112)
using these values in equation (112), we get
(113)
so
(114)
for some ij which is a contradiction to the fact that is a neighborly totally irregular neutrosophic cubic graph. Hence,
(115)
so is neighborly irregular. This proves the result.

Proposition 40. If is neighborly irregular as well as neighborly totally irregular, then

(116)
need not be a constant function.

Remark 41. If is neighborly irregular, then a neutrosophic cubic subgraph H of may not be neighborly irregular.

Remark 42. If is neighborly totally irregular, then a neutrosophic cubic subgraph H of may not be neighborly totally irregular.

Details are in the caption following the image
Represents a complete neutrosophic cubic graph, but not neighborly irregular.

4. Applications

As neutrosophic cubic graph theory is a developing field of modern mathematics, it has many applications in different fields. In this section, we discuss applications of neutrosophic cubic graphs in finding the effects of different factors in the neighboring countries of Pakistan. Further, we used our proposed model in decision-making while selecting a house in a certain locality.

We will use the following proposed algorithm in the following real-life problems.

Step 1. Calculate the memberships, indetermined-memberships and falsity membership for corresponding vertex in vertex set A in interval form as well as in the ordinary fuzzy set.

Step 2. Calculate the neutrosophic cubic open neighborhood degree of a vertex.

Step 3. Calculate the neutrosophic cubic closed neighborhood degree of the same vertex.

Step 4. Comparison between degrees provided in Steps 2 and 3.

The frame diagram to clarify the organization of the proposed method is given in Figure 11.

Details are in the caption following the image
Frame diagram of the proposed method.

4.1. Effects of Different Factors on the Neighboring Countries of Pakistan

Suppose we are interested to check the effects (e.g., time/durations/situations) on different factors in the neighboring countries of Pakistan. These factors may be the population, literacy, health conditions, etc., of these countries. So, we take Pakistan and its neighboring countries as a set of vertices and link between these countries through roads as our edge set. Hence, graph G = (A, B) has set of vertices A = {Pak, Ir, In, Ch, Af}, where Pak stands for Pakistan, Ir for Iran, In for India, Ch for China, and Af for Afghanistan. Let the set of edges be E = A network of roads between these countries, so we can define membership function for each vertex vA to denote strength or degree of these vertices as Φ(v) = {Pop, PCI, LR, WTRU, HE, PSI}; here, we have three different categories/situations/time/duration say past, future, and present. Also, here, interval membership represents past and future for truth and indeterminate membership, respectively, and present time represents falsity memberships.
  • (i)

    Pop represents interval membership for the population of a country in the duration (1st July 2018, 1st July 2019)/max population of the corresponding country in the same duration. Here, interval represents past and future to represent truth membership and indetermined-membership for members of the vertex set A

  • (ii)

    PCI is for per capita income of a country which represents falsity membership for the corresponding vertex in vertex set A

  • (iii)

    LR represents interval membership for literacy rate of a country in the duration [2011, 2014]. Here again, we have interval to represent past and future for truth membership and indetermined-memberships for members of vertex set A

  • (iv)

    WTRU is the position of the corresponding country in the world’s top-ranking universities/max number of universities in these countries

  • (v)

    HE represents interval membership for %age of health expenditure of a country in the duration [2010, 2015] where interval shows past and future to represent truth membership and indetermined-membership for members of vertex set A

  • (vi)

    PSI is the number of popular sports interest/max number of sports played in the corresponding country as health depends on sports

By data collection for different time intervals, we have neutrosophic cubic membership for vertex set A represented in Table 8.

Table 8. A neutrosophic cubic representation for vertex set A.
A Pop [past, future] PCI (present) LR [past, future] WTRU (present) HE [past, future] PSI (present)
Af [.026.026] .06 [.32.38] .01 [.92.82] .13
Ch [1, 1] 1 [.92.96] 1 [.49.55] .67
In [.94.95] .212 [.69.72] .21 [.43.47] 1
Ir [.057.058] .57 [.86.95] .123 [.81.69] .2
Pak [.148.151] .162 [.55.57] .06 [.31.26] .33
The neutrosophic cubic open neighborhood degree of a vertex (say Pak) is
(117)
also, the neutrosophic cubic closed neighborhood degree for the same vertex is defined as
(118)

The neutrosophic cubic open neighborhood degree of a vertex (say Pak) is less than the neutrosophic cubic closed neighborhood degree of a vertex (say, Pak). Thus, we may conclude that the vertex (say, Pak) has more closed neighborhoods than the open neighborhoods that can change their loyalties according to time as shown in Figure 12. Similarly, we may check other countries.

Details are in the caption following the image
Represents neighborhood of Pakistan.

4.2. Decision-Making while Selecting a House

Suppose we are interested to purchase a house in a housing society. Then, we have to consider certain features before making our final decision like availability of mosque, workplace, school, college, university, clinic/hospital, market, park, and gym, width/condition of roads, and the distance of the house and all these facilities. We also keep in view past, future, and present situations of all these attributes, or we keep in view trends and demands and check effects of duration on these areas. So, we take a survey of different areas in a locality and take a set of different houses with different features as our set of vertices and link or distance between these as our edge set. Let h1, h2, h3, h4 be different choices of houses, and we define neutrosophic cubic membership function of a house hV as
(119)
here, interval membership represents past and future for truth and indeterminate membership, respectively, and present time represents falsity memberships, and
(120)
Let
(121)
Then, the neutrosophic cubic open neighborhood degree of each vertex (house) is given for house h1; we have
(122)
similarly, for house h2, we have
(123)
and for house h3, we have
(124)
also, for house h4, we have
(125)
Also, the neutrosophic cubic closed neighborhood degree of each vertex (house) is given for house h1; we have
(126)
and for house h2, we have
(127)
and for house h3, we have
(128)
Also, for house h4, we have
(129)

We compare neutrosophic cubic open neighborhood and observe that neutrosophic cubic open neighborhood of h2 and h3 is the same, but comparison of neutrosophic cubic open neighborhood of h1 and h4 shows that h1 and h4 are more effective than h2 and h3. Also, we observe that neutrosophic cubic open neighborhood of h4 is effective than h1. Moreover, one can observe that the neutrosophic cubic closed neighborhood degree of houses h1 and h4 is the same, but in view of neutrosophic cubic open neighborhood, h4 is the best choice in all respects as compared to other houses h2 and h3. So, choice of house h4 is the best choice for our selection of an ideal house. Position of four houses with different facilities is shown in Figure 13.

Details are in the caption following the image
Represents position of four houses with different facilities.

5. Comparison Analysis

In this paper, our focus is to introduce some different types of neutrosophic cubic graphs. These include balanced, strictly balanced, complete, regular, totally regular, and irregular neutrosophic cubic graphs. In this regard, we explained the open and closed neighborhood of a vertex of the neutrosophic cubic graph and its role in the art of decision-making. Many of these graphs have already been discussed from a different perspective by the other researchers, for example, Poulik et al. [2932], Akram [44, 45], and Gulistan et al. [65]. We have tried to discuss them concerning the neutrosophic cubic graphs. The neutrosophic cubic graphs are the generalization of different versions of the fuzzy graph which is extended to the neutrosophic cubic graph. The idea is summarized in the form of a flow chart (Figure 14).

Details are in the caption following the image
Flow chart of comparison analysis.

This flow chart shows under certain conditions neutrosophic cubic graphs reduced to crisp graphs. So, under certain conditions, all the different types described are reduced for neutrosophic graphs, cubic graphs, intuitionistic graphs, fuzzy graphs, and crisp graphs.

6. Conclusion and Future Work

In this article, we provided different types of neutrosophic cubic graphs with examples and give many results which correlate with these neutrosophic cubic graphs. We used the idea of the neutrosophic cubic open neighborhood degree and neutrosophic cubic closed neighborhood degree of the same vertex in two real-life problems. We concluded the following: (1) As the neutrosophic cubic open neighborhood degree of a vertex (say Pak) is less than the neutrosophic cubic closed neighborhood degree of a vertex (say, Pak), the vertex (say, Pak) has more closed neighborhoods than the open neighborhoods. (2) Also, we observe that house h4 is the best choice for our selection of an ideal house using the idea of neutrosophic cubic open neighborhood degree and neutrosophic cubic closed neighborhood degree of the same vertex. The limitation of the presented method is the data collection which is not an easy task. In the future, we aim to make more different types of graphs such as line, planer, and directed neutrosophic cubic graphs. We are also aiming to have more real-life applications of neutrosophic cubic graphs.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors express their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the Public Research Project under Grant Number (R.G.P.1/208/41).

    Data Availability

    There is no data related to this article.

      The full text of this article hosted at iucr.org is unavailable due to technical difficulties.