1. Introduction
Domination and its variations in graphs have been extensively studied
[1, 2]. For a graph , a set is a domination set if each vertex in
is adjacent to a
vertex in . The domination number
is the minimum
cardinality of a dominating set of . We call a dominating set of
cardinality a -set. For subsets , the set is said to dominate if every vertex in is adjacent to a vertex in .
Domination describes situations where every unoccupied vertex
requires a guard in its adjacent vertices. In such scenarios, only one
type of guard is assumed. Consider a more complex scenario where there
are multiple types of guards (let’s say types), and each vertex that lacks a
guard must have all types of guards in its adjacent vertices.
From a practical perspective, consider a workshop with mechanics performing the same task,
each requiring different tools.
Why should only a mechanic with no tools have access to all the tools in
their adjacent vertices? Instead, this condition should apply to all
mechanics. In other words, a mechanic with one, two, three, or any
number of tools should have access to all required tools around them. Therefore,
we propose a modification to the definition in the following article:
“All graph vertices must see all labels in their neighborhood.” This
relaxation leads to the following definitions:
Let be a graph and let be a function assigning to each vertex
a subset of colors selected from the set ; that is, .
If for every vertex such
that , we have .
A type of domination in graphs: Consider a set of colors assigned randomly to each vertex
of a graph . If we require that
every vertex with an empty set assigned to it must have all colors in its neighborhood, then this
is called the -rainbow dominating
function of a graph . The
parameter , which is the
minimum total number of colors selected over all vertices of , is called the -rainbow domination number of .
Definition 1. Assume is a graph and let be a function that assigns to each
vertex a set of colorful items selected from the set ; that is, . A function is
described as a generalized -rainbow dominating function () of if for each vertex , we have .
Therefore, is named a generalized
-rainbow dominating function
() of . The weight of a function is defined as .
Given a graph , the minimum weight
of a is called the
generalized -rainbow dominating
number of , denoted by .
The following theorem is simple.
Theorem 1. Assume is a graph. Then,
2. Generalized -Rainbow Domination in Graphs
Proof.
To check for
graphs , where , we can
label and the rest
of the vertices as . Then,
and .
Generally, for graphs , where with not
adjacent to and vice versa, we
can label , , , and . Since and , and , at least one of the
remaining vertices must have , say , and then for the same
reason, . Therefore, if
the rest of the vertices are , then .
For graphs or star
graphs, is .

Definition 2. A tree graph with vertices having pendant vertices of degree , and the initial and final vertices
of the graph have degree , is
denoted by .
Theorem 2. For the tree graphs , .
Lemma 2. For the paths , we have
for if , then ,
for if and , then .
Lemma 3. For the cycles , we have
for if , then ,
for if and , then ,
for if and , then .
2.1. for graphs
Definition 3. Assume and be initial natural numbers . The generalized Petersen is defined as follows. Let , be two decompositions of length
. Suppose the vertices of are and edges for and . Assume the vertices of are and edges for , the sum being derived modulo (). The graph is obtained from . It’s clear that . The graph or is the famous Petersen
graph.
Theorem 3. For graphs with , we have
Proof. We use the following partition of :
a) If , then
. We use the following
algorithm to define the function
on where :
if and if .
for , for .
In the graph , the outer
circle consists of vertices with multiples of labeled , resulting in for the outer circle equal
to , and the inner circle consists of vertices with
multiples of labeled , resulting in for the inner circle equal
to . Therefore, .
b) If , then
we use the following algorithm to define the function on where :
if and , if , and .
for , for .
In the graph , the outer
circle consists of vertices with multiples of and the vertex labeled , resulting in for the outer circle equal
to , and the inner circle consists of vertices with multiples of
labeled , resulting in for the inner circle equal
to . Therefore, .
c) If , then
we use the following algorithm to define the function on where :
if and , and if .
for , for .
In the graph , the outer
circle consists of vertices with multiples of and the vertex labeled , resulting in for the outer circle equal
to , and the inner circle consists of vertices with multiples of
and the vertex labeled , resulting in for the inner circle equal
to . Therefore, .

Theorem 4. For graphs where , we have:
Proof. We use the following partition of :
a) If , we
use the following algorithm to define the function on where :
In the graph , the
vertices for in the outer circle
are labeled , and the rest
of the vertices are labeled . This results in for the outer circle equal
to .
Vertices for in the inner circle
are labeled , and vertices
for are also labeled . The rest of the vertices are
labeled . This results in
for the inner circle
equal to . Therefore, .
b) If , we
use the following algorithm to define the function on where :
In the graph , the
vertices for in the outer circle
are labeled , and the rest
of the vertices are labeled . This results in for the outer circle equal
to .
Vertices for in the inner circle
are labeled , and vertices
for are also labeled . The rest of the vertices are
labeled . This results in
for the inner circle
equal to . Therefore, .

Definition 4. The honeycomb structure has six sides. The honeycomb is formed by appending six hexagons
to the outer edges of .
Similarly, the honeycomb system is obtained from by adding six sides around the
perimeter of . The number of
vertices and edges in are
and , respectively. In graph theory,
the honeycomb system can be represented as a brick structure by
collapsing one set of opposite vertices along the direct lines,
resulting in the same number of vertices and edges. The honeycomb
structure is widely used in various applications such as all-to-all
broadcasting in computer networking and in chemistry to model the shape
of various compounds.
Lemma 4. [3] The boundary of is the cycle .
Theorem 5. For the honeycomb network , we have
Proof. According to Lemma 1, and , and so on, where . To find the
generalized -rainbow dominating
number of the honeycomb network , we label each cycle separately up
to the th cycle. Then, we
calculate for each
cycle and add them together. The labeling algorithm starts by labeling
all the vertices of the first cycle with , so .
To label the second cycle (without considering subsequent cycles), we
select an arbitrary vertex with degree and label it , and then label the rest of the
vertices in the cycle in the same pattern as the first cycle. The
vertices are labeled as follows:
According to Lemma 3, . So, for
being the number of cycles,
Then,
This process continues until 
3. Conclusion and Future Works
In this paper, we have extended the concept of -rainbow domination to make it more
applicable in various fields while reducing associated costs. Instead of
requiring vertices with empty labels, we introduced a new condition that
each vertex must have neighbors.
This generalized -rainbow
domination was applied to simple graphs as well as honeycomb
networks.
Moving forward, there are several avenues for future research. One
potential direction is to extend the concept of generalized -rainbow domination to honeycomb
networks and develop different algorithms to solve related problems.
Conflict of
Interest
The authors declare no conflict of interest.