1. Introduction
Graph theory (GT) has attracted increasing attention because of its wide range of practical applications and importance in both mathematics and the practical sciences. A graph with as the set of vertices and as the set of edges that reflect the connections between these vertices is called an ordered pair. The size of the edge set, , indicates the number of edges, and the order of the vertex set, , determines the order of the graph.
GT can be generically classified into two groups. The first focuses on undirected graphs, which allow for unrestricted movement between connected vertices since connections between them do not have direction. The second category includes directed graphs, where each edge is assigned a particular direction. Whereas edges in undirected graphs are regarded as unordered pairs, edges in directed graphs are represented as ordered pairs.
Leonhard Euler became renowned as the Father of GT in 1736 after solving the famous Konigsberg Bridge Problem. The graphical aspect of this theory has given rise to a variety of applications, such as frameworks for decision-making, the visualization of object relationships, and a flexible method for adjusting and changing current systems. In chemical sciences [
1,
2], data science [
3], and architecture [
4], GT has diverse applications. Furthermore, Jachymski [
5] developed several FP results by applying the graph structure in FPT. Shukla et al. [
6] extended a number of FP discoveries in GMSs and applied the graph structure to MSs. Chen et al. [
7] introduced the idea of a graphical convex MS and produced FP results for set-valued G-contraction mappings and related applications.
By introducing fuzzy set theory (FST), Zadeh [
8] developed a framework for handling the imprecision and uncertainty that are frequently encountered in day-to-day living. Building on this, FMS was introduced by Kramosil and Michalek [
9] using MSs and fuzzy sets. In order to improve the FMS framework, George and Veeramani [
10,
11] added a Hausdorff structure for these spaces. The concept of graphical fuzzy metric spaces (GFMSs) was first proposed by Saleem et al. [
12] and used in the fractional differential equation analysis. This idea was then expanded and refined by Shukla et al. [
13] through their investigation of fixed point theory (FPT) in GFMSs.
In their work, Huang and Zhang [
14] developed the concept of cone metric spaces (CMSs) by substituting the set of real numbers with an ordered Banach space, and they established various FP results for contractive mappings within this setting. Tarkan Oner et al. [
15] introduced the concept of FCMSs, which generalizes CMSs, and established some FP results. Satish Shukla et al. [
16] introduced the concept of GCMSs over Banach algebras and established several FP results for specific types of contractive mappings defined on these spaces.
In this study, we introduce the concept of graphical fuzzy cone metric spaces (GFCMSs) and investigate their topological properties in depth. Furthermore, we establish new FP theorems within the framework of complete GFCMSs. The integration of fuzzy, cone, and graph structures into a unified framework is driven by the need to handle uncertainty, partial ordering, and relational dependencies simultaneously in metric-type spaces. Fuzzy metrics account for the vagueness and imprecision inherent in many real-world situations, while cone metrics introduce a vector-valued structure that allows for more refined comparisons, especially in ordered Banach spaces. Incorporating a graph structure enables modeling of asymmetric relationships and directional constraints among elements, which are common in applications such as network theory, optimization, and decision processes. By combining these three components, graphical fuzzy cone metric spaces provide a more flexible and robust setting for analyzing convergence, continuity, and fixed point behavior in complex systems that cannot be effectively studied under classical or single-component metric frameworks.
2. Preliminaries
Definition 1. Let be a real Banach algebra, meaning that is a real Banach space equipped with a multiplication operation that meets the following requirements (see [17]): ∀
, , - 1.
;
- 2.
and ;
- 3.
;
- 4.
.
In this paper, we work under the assumption that
is a Banach algebra possessing a unit element; that is, there exists an identity element
e in
satisfying
for every
. An element
is called invertible if there exists
s.t.
. The inverse of an element
is represented by
. The quantity
denotes the spectral radius of the element
in
and
Definition 2 (Liu and Xu [
18])
. Let be a subset of . The set is defined as a cone provided it satisfies the conditions below:- 1.
is non-empty, closed, and distinct from the singleton set ;
- 2.
For any non-negative real numbers and any elements , the linear combination belongs to ;
- 3.
If and simultaneously , then it must follow that .
A cone is said to be solid if its interior is nonempty, i.e., . We consistently assume that the cone in question is solid. In a Banach algebra , each cone defines a partial order ⪯ on s.t. for all , the relation holds precisely when lies in . Also, we write if and only if int for all .
Remark 1 (Jungck et al. [
19])
. Let be a cone of , and .- 1.
If and , then .
- 2.
If and , then .
- 3.
If for every , then .
Remark 2. For any , ∃ s.t. , and for any , we can find satisfying .
Definition 3 (Dordević et al. [
20])
. A sequence within the cone is called a c-sequence if, for every with , ∃
s.t. ∀
. Definition 4 ([
15])
. A 3-tuple is defined as a fuzzy cone metric space (FCMS) if the following hold: is a cone within the space , Ω
is a set chosen arbitrarily, ∗ represents a continuous t-norm, and is a fuzzy set on the product space that satisfies specific conditions.For all and (that is )
- 1.
,
- 2.
,
- 3.
,
- 4.
,
- 5.
is continuous.
We modify certain concepts related to graphs based on the work of Jachymski [
5] and Shukla et al. [
6].
For a non-empty set , ∆ represents the diagonal of , and A directed graph is denoted by if its collection of vertices is and its set of edges is , which includes no parallel edges and . In this context, we say that is equipped with the graph . The symmetric graph, denoted by , is defined as , where is characterized by and .
Let . A sequence of vertices , taken from , is said to be a path of length from to if it satisfies , , and for every index , the pair belongs to . The vertices and are said to be connected if a path of any length connects to . The graph is considered connected if, for any two vertices in , there is a path that links them.
We define for . Additionally, we introduce a relation P on such that holds if and only if and are connected by a directed path in . We denote if lies along a directed path from to in . A sequence in is said to be -termwise connected if, for every , the relation holds.
Definition 5 ([
16])
. Let Ω
be a non-empty set equipped with the graph , and let be a Banach algebra. Assume that a mapping satisfies the following conditions:- 1.
∀;
- 2.
;
- 3.
∀;
- 4.
, implies ∀.
In this case, the mapping is referred to as a graphical cone metric on Ω, and the pair is called a GCMS over the Banach algebra .
Next, we define the concept of graphical fuzzy cone metric spaces (GFCMSs).
3. Graphical Fuzzy Cone Metric Spaces
Definition 6. Let Ω be a nonempty set equipped with the graph , and let be a Banach algebra, ∗ be a continuous t-norm, be a cone in , and be a fuzzy set on . The function is referred to as a graphical fuzzy cone metric provided that the following conditions are satisfied.
- 1.
;
- 2.
;
- 3.
;
- 4.
, implies ;
- 5.
is continuous,
for all and . Then, is known as a GFCMS.
If we take , and , then every GFMS becomes a GFCMSs.
Example 1. Consider , where the norm is Euclidean and multiplication is defined component-wise. Define then is a solid cone in . Let and set to be and . Define a function byThen, is a GFCMS over Banach algebra . Example 2. Consider , where the norm is Euclidean and multiplication is defined component-wise. Define then is a solid cone in . Let and set to be and at time t. Let ∗ be a continuous t-norm defined by . We define a function as follows: Afterward, we demonstrate that forms a GFCMS over a Banach algebra. The properties 1, 2, and 3 are obvious. For 4, suppose and , i.e., . Then, we must have . This shows that . On the other hand, is not a FCMS, as .
GFCMS differs from GFMS by allowing for cone-valued time parameters ordered via a cone in a Banach algebra, introducing a partial order into the triangle inequality and enabling comparisons within a richer, vector-valued fuzzy framework.
Definition 7. Consider as a GFCMS. Let be a function, and let represent a subgraph of with the property that contains Δ. In this setting, the mapping is referred to as a -graphical fuzzy cone contraction, provided the following conditions are satisfied:
- (GFCC1)
implies , i.e., is edge-preserving in
- (GFCC2)
for some constant κ with s.t.
∀ and with .
Example 3. Let , and let be equipped with the conewhich is a solid, normal cone in the Banach algebra under coordinate-wise operations. We define the continuous t-norm ∗ by . We define a graph on Ω
such thatLet be the subgraph of defined as follows: We define the function as follows: Now, we define the mapping as follows: We now verify that satisfies both conditions of Definition 7:
- (GFCC1)
Let , i.e., . Then,
so . Hence, is edge-preserving in .
- (GFCC2)
Let , i.e., . Then,
Since for all , the inequality holds for some . It follows that Hence, satisfies (GFCC2).
Thus, is a -graphical fuzzy cone contraction.
The contraction behavior of the mapping
as a graphical fuzzy cone contraction in GFCMS is depicted in
Figure 1.
Definition 8. Let be a GFCMS. Given , the open ball centered at ζ with radius is described as Theorem 1. Let be a GFCMS. We defineThen, τ defines a topology on Ω
. Proof. It is clear that ; hence, . Suppose . Let . Then, there exist such that and . Since , ∃ s.t. and and so . Hence, . In the same way, it can be demonstrated that the union of any family of sets from also belongs to . □
Theorem 2. Let be a GFCMS. Then, is a Hausdorff.
Proof. Let such that . Based on the definition of a fuzzy metric, we have , and let us denote by r. According to Remark 2, for every satisfying , there exists a value such that .
Now, consider the sets
and
. We have to see
Suppose that
. Then, there exists
. Therefore,
and
Thus,
. Then,
, so
This is a contradiction. Hence,
. □
Definition 9. Let be a GFCMS. Then, the graphical fuzzy cone distance between and a set is defined by, and the distance between is defined by We shall use to represent the family of all nonempty compact subsets of .
Example 4. Let , , and let be a solid cone in the Banach algebra (with component-wise operations). Let ∗ be the product t-norm: .
We define the graph on Ω such that: .
We define the graphical fuzzy cone metric as follows: Now, take , . Since means (based on the graph structure), we compute the following: .
Now, let . Then, .
We already have
so .
Definition 10. Let be a GFCMS, then the graphical fuzzy cone Hausdorff distance between two elements and of is as follows: Thus, , equipped with the graphical fuzzy cone Hausdorff distance, forms a GFCMS. This space will be referred to as the graphical fuzzy cone Hausdorff metric space (GFCHMS) and will be denoted by . It should be noted that the graphical fuzzy cone Hausdorff metric space is complete, provided that the GFCMS is complete.
Lemma 1. Let be a -graphical fuzzy cone contraction (GFCC) on a GFCMS . Then, defined byis a -GFCC on . Proof. Suppose
is a
-GFCC on a GFCMS
. Then,
is edge-preserving in
, and there exist
s.t.
∀
and
with
. Since
is a
-graphical fuzzy cone contraction on a GFCMS
,
maps
into itself. We will demonstrate that
is a
-graphical fuzzy cone contraction on
. Clearly,
is edge-preserving in
. For any
and
, let us have the following:
Similarly,
Equation (1) implies that
Thus,
is a
-graphical fuzzy cone contraction on
. □
Definition 11. Let be a GFCMS. A sequence in Ω is said to converge to with respect to if, for every and each , ∃ s.t. holds ∀; that is, for every . A sequence in Ω is termed a Cauchy sequence if, for every and each , ∃ s.t. ∀ indices .
Example 5. Let , and define the solid cone in . Define the graph on Ω
as follows: Define the graphical fuzzy cone metric as follows: Now, define the sequence for . Clearly, and as .
To check convergence, let and fix . Choose such that for all :Then, for , we have the following:Thus, in the GFCMS. To show that is Cauchy, fix and choose such that for all ,Since or (as both approach 1 from below), the ordered pair lies in , so:Hence, is a Cauchy sequence in the GFCMS. The convergence of the sequence
to 1 in the GFCMS is illustrated through the graphical fuzzy cone metric
in
Figure 2.
Definition 12. Let be a GFCMS. A space is considered complete if all Cauchy sequences in Ω converge to an element within Ω. Let be a subgraph with . We describe as -complete whenever every -termwise connected Cauchy sequence in Ω tends to a point in Ω.
Definition 13.
Let be a GFCMS and let be a subgraph of such that . A sequence in is said to be -convergent to if, for every and , there exists such that and for all . Moreover, if for all , then the sequence is said to be -convergent to .
It is straightforward to verify that sequences that are
-convergent are also
-convergent; however, the converse is not generally true. Next, we proceed to explore the notion of Cauchy sequences within GFCMSs. In the framework of FMSs, the ideas of Cauchy sequences and completeness have been introduced in two distinct approaches: one following Grabiec (refer to [
21]) and the other based on the work of George and Veeramani (refer to [
10]). In a similar fashion, we present two categories of Cauchy sequences along with their corresponding forms of completeness.
Clearly, the condition of -completeness is less restrictive compared to the overall completeness of a GFCMS.
In the following section, we present several FP theorems for self-maps on a GFCMS under specific assumptions.
Definition 14. Let be a GFCMS and be a sequence in . The sequence is referred to as a Cauchy sequence in if, for every , it satisfies either of the following equivalent conditions:
- 1.
For any natural number p, the limit equals 1 (i.e., -Cauchy).
- 2.
The limit (i.e., -Cauchy).
Definition 15. Let be a GFCMS and be a sequence in Ω
. The sequence is described as graphical fuzzy cone contractive. Assuming the existence of a constant s.t.
∀
, . Example 6. Let , , and define the solid cone in the Banach algebra . Let the graph on Ω
be defined as follows: We define the fuzzy cone metric by Now, we define the sequence for . Then, Let us compute the quantities: Thus:which holds for some and all n because the exponential function is increasing and . Hence, the sequence satisfies the graphical fuzzy cone contractive condition.
Definition 16. Let be a GFCMS, and let be a subgraph of s.t. . The space is referred to as --complete, --complete, or --complete (alternatively, --complete, --complete, or --complete) if every -termwise connected -Cauchy sequence (or -Cauchy sequence) within Ω converges in the sense of or is -convergent or -convergent, respectively.
Theorem 3. Consider as a --complete GFCMS, where every graphical fuzzy cone contractive sequence is also a -Cauchy sequence. Let be an -GFCC. Consider that the conditions listed below are fulfilled:
- 1.
∃ s.t. for some ;
- 2.
∀ s.t. ;
- 3.
if a -Picard sequence , which is -termwise connected, converges to some with respect to , then it also converges to ζ in the sense of .
As a result, one can find an element such that the Picard sequence generated by , starting from an initial point , is -termwise connected and converges in the sense of to both and .
To prove the existence of a fixed point, we construct a Picard sequence starting from a point and show that it is -termwise connected, graphical fuzzy cone contractive, and converges to a common point and under -convergence.
Proof. Consider a point
for which
belongs to the
q-step neighborhood
, where
q is a positive integer. Assume that
represents the Picard sequence generated by
, starting from the initial point
. According to the definition, there exists a sequence
satisfying
,
, and
for each
. As
is an
-GFCC, based on
, we have
for
. Therefore,
is a path from
to
of length
q, and so
. Thus, for every
, we can build a sequence
connecting
to
, having length
q. This demonstrates that
for all
. Thus,
is an
-termwise connected sequence. Since
for
and
based on
, we have the following:
∀
. As the sequence
is
-termwise connected, it follows that for each
, we have the following:
. It follows that
is a graphical fuzzy cone contractive sequence and, by assumption, is
-Cauchy in
. Consequently,
is a
-termwise connected sequence that is also
-Cauchy in
. Based on the
-
-completeness of
, the sequence
-converges to some
. Additionally, from Condition (3), it follows that
converges to
under
-convergence. Then, ∃
s.t.
∀
and
Thus, based on
, we obtain
for each
, and using
, we get
∀
. Since
we get
Therefore, the sequence
also
-converges to
. Consequently, the sequence
-converges to both
and
. □
Corollary 1. Let be a --complete GFCMS and be a GFCC. Consider that the conditions listed below are fulfilled:
- 1.
∃ s.t. , for some ;
- 2.
∀ s.t. .
There exists an element s.t. the -Picard sequence , initiated at , is -termwise connected and converges to both and with respect to the -convergence.
It is important to note that the previous results guarantee the convergence of a -Picard sequence, but they do not guarantee the existence of a FP for , as demonstrated in the following example.
Example 7. Consider endowed with the Euclidean norm and the operation of coordinate-wise multiplication. Define then is a solid cone in . Let and be defined by and at a time t. Let ∗ be a continuous t-norm defined by . Define a function byThen, is a GFCMS over Banach algebra. Suppose a self-map is given:It is straightforward to observe that is an -graphical fuzzy cone contraction with a constant κ s.t. . For any initial point , we have , meaning that , where . Furthermore, it can be verified that all the hypotheses of Theorem 3 are fulfilled but does not have a FP in Ω
. The previous example demonstrates that even when all the hypotheses of a fixed point theorem are satisfied, a fixed point may still fail to exist if the limit of the Picard sequence is not unique. In particular, the sequence may converge to two different points—one in and another in the image of under . To eliminate such pathological cases and guarantee the existence of fixed points, we now introduce an additional condition known as Property .
Definition 17. Let be a GFCMS, be a subgraph of , and be a mapping. The five-tuple is said to possess property if a -termwise connected -Picard sequence happens to converge to two different points,We represent by the collection of all FPs of the mapping . In addition, let us define Theorem 4. Let be a --complete GFCMS in which graphical fuzzy cone contractive sequences are -Cauchy. be an -GFCC. Consider that the conditions listed below are fulfilled:
- 1.
∃ s.t. for some ;
- 2.
∀ s.t. ;
- 3.
if a -termwise connected -Picard sequence -converges to some , then -converges to ζ.
We can identify a point s.t. the -Picard sequence , starting with the initial value , is -termwise connected and converges to both and with respect to . Furthermore, if the five-tuple satisfies property , then has a FP in Ω.
Proof. The proof of this theorem follows directly from the argument presented in Theorem 3 and is therefore omitted.□
The following example illustrates that Theorem 3 provides a sufficient condition for the existence of a FP, but it does not ensure the uniqueness of the FP.
Example 8. Let , and let be the graph with vertex set andand . Then, is a GFCMS, where is defined as follows:Then, is a -complete GFCMS. Suppose a self-map is given:It is straightforward to observe that is a GFCC with a constant κ s.t. . For each , we have and . Additionally, it is clear that for all . It is important to note that property is also fulfilled. Therefore, all the conditions of Theorem 3 are met, and the mapping has two FPs in Ω
, specifically, Fix. Theorem 5. Let be a --complete GFCMS in which graphical fuzzy cone contractive sequences are -Cauchy. Let be an -GFCC. Provided that the assumptions of Theorem 4 are fulfilled and is connected, has a unique FP.
Proof. The existence of a FP of
can be derived from Theorem 4. Assume that
is connected, and let
and
be two distinct FPs of
. Since
,
, and so
. Again,
is connected, and we have
; that is, we can identify a sequence
,
and
for
Since
is an
-GFCC, through successive use of
, we have
for
∀
. Therefore, based on
, we obtain
for
and for all
. Therefore, based on Condition (4) of GFCMS, we obtain the following:
Since
, we have
; by taking the limit as
, the above inequality implies that
∀
. This leads to the conclusion that
. This contradiction establishes the uniqueness of the FP of
. □
Corollary 2. Let be a -complete GFCMS s.t. ∀
. Suppose a self-map is given:
∀
and . Then, has a unique FP. Proof. Let the graphs and be defined s.t. and . It is then straightforward to verify that all the conditions of Theorem 5 are met, and consequently, the conclusion holds. □
Although fixed point theorems are well-studied, the present work offers a new and meaningful generalization within the framework of graphical fuzzy cone metric spaces (GFCMS). In particular, Theorem 5 establishes a unique fixed point result for -graphical fuzzy cone contractions by employing both fuzzy metric structures and graph-based connectivity. The incorporation of graph completeness, fuzzy cone metrics, and generalized convergence through Property (S) and -connectivity significantly extends existing results. This allows us to derive fixed point theorems in settings where classical or even fuzzy contractions may fail, thereby highlighting the novelty and broader applicability of our results.
4. Application to Fractals
In this section, we will generate fractals in graphical fuzzy cone metric space using a graphical fuzzy cone iterated function system. This is entirely novel work, as no one has yet generated fractals in a graphical structure. While the process of generating fractals resembles classical iterated function systems, the advancement here lies in extending the framework to fuzzy cone metric spaces equipped with a graph structure. By introducing the concept of a Graphical Fuzzy Cone Iterated Function System (GFCIFS), we move beyond traditional metric-based models and capture a broader class of fractal behaviors. The use of fuzzy cone metrics allows us to incorporate uncertainty and partial ordering, while the graph component models directional relationships and local connectivity among elements. This setting enables the construction of fractals in environments that are not necessarily metrically uniform or linearly connected, thereby offering a more flexible and realistic approach to modeling self-similar structures in abstract spaces.
Definition 18. Let be a GFCMS and is a finite collection of graphical fuzzy cone contractions (GFCCs). The operator defined byfor all is a graphical fuzzy cone Hutchinson Barnsley operator (GFCHBO). Definition 19. Let be a complete GFCMS. If is a finite collection of GFCCs, then is called a graphical fuzzy cone iterated function system (GFCIFS).
Theorem 6. Let be a GFCMS and be a GFCIFS. If is GFCHBO, then Θ is GFCC on .
Proof. We will show that
is GFCC on
for
. Let
be two GFCCs. Therefore,
and
will map
into itself. Take
such that
is not equal to zero. Consider the following:
Similarly, this can be proven for any natural number
t. Hence,
is a GFCC on
. □
Theorem 7. Suppose that is a --complete graphical fuzzy cone Hausdorff metric space in which graphical fuzzy cone contractive sequences are -Cauchy with the property , and is a GFCIFS. Let be GFCHBO. Assume that the following conditions hold:
- (i)
, provided there exists for some ;
- (ii)
for all ;
- (iii)
If a sequence , which is -termwise connected, is Picard convergent in , then ∃ a limit and s.t. or at time t ∀ ;
- (iv)
is connected.
Then, has a unique FP.
Proof. Since is GFCHBO, it follows from Theorem 6 that is a graphical fuzzy cone contraction on . Now, if we take , and in Theorem 5, then the proof is similar. □
Example 9. Let and be a graph defined by and . Let be a subgraph of a graph s.t. and Define then is a solid cone in Ω
. Then, is a GFCMS, where is defined as follows:Then, is -complete GFCMS. Let and We aim to demonstrate that the mappings and are edge-preserving. Let and be elements of Ω
, and assume there exists a path between these two points. This implies the ordering relationships:Therefore,Since these inequalities hold, the transformation preserves the adjacency and connectivity of the edges. By applying the same reasoning, we can similarly conclude that and also preserve edge relations. Thus, all three mappings are edge-preserving mappings.
Let , thenSimilarly, we can conclude that also satisfy the second condition of Definition 7. Hence, , and are GFCCs. Consider the GFCIFS with the mapping given asfor all . Based on Theorem 7, we have the following:As a result, all conditions outlined in Theorem 5 are satisfied. Additionally, the sequence of compact sets converges, and its limit represents the attractor of Θ
for any initial set . The step-by-step construction of the fractal set under the iteration of
is illustrated in
Figure 3.
We provided a detailed example to demonstrate the process of constructing fractals within a graphical fuzzy cone metric framework. In this example, we used three specific mappings on the space , each of which preserved the structure of a graph that captures partial ordering in both coordinates. The fuzzy cone metric was defined through an exponential function involving the Euclidean distance, allowing us to incorporate uncertainty in a natural way. It was shown that the mappings , , and preserve the edges of the graph, meaning that they are consistent with the directional relationships of the structure. Additionally, these mappings satisfy a contractive condition specific to the graphical fuzzy cone setting, which means that the system forms a well-defined Graphical Fuzzy Cone Iterated Function System (GFCIFS). The associated operator , defined by the union of these mappings, was shown to meet a contractive requirement with respect to the graphical fuzzy Hausdorff metric , which ensures the existence of a unique fixed set, called an attractor. This attractor arises as the limit of an iterative sequence starting from any initial compact set. Unlike classical fractals, this attractor incorporates the influence of both fuzzy behavior and graph-based structure, providing a more general setting for modeling self-similar patterns. This example confirms that our approach can be used to construct and analyze fractals in spaces where uncertainty and directional relationships play a key role.