1. Introduction
A graph is a pair
, where V is the set of vertices (usually
or a subset thereof) and E is the set of edges (an edge is a two-element subset of vertices); what we call a graph is sometimes called a simple undirected graph. In this paper each graph is finite and has nonempty vertex set. The order of a graph G, denoted
, is the number of vertices of G. A path is a graph
such that
.
A cycle is a graph
such that
. The length of a path or cycle is the number of its edges. A complete graph is a graph
such that
. A graph
is bipartite if the vertex set V can be partitioned into two nonempty subsets U and W, such that every edge of E has one endpoint in U and one in W. A complete bipartite graph is a bipartite graph
such that
,
and
.
The line graph of a graph
denoted
, is the graph having vertex set E, with two vertices in
adjacent if and only if the corresponding edges share an endpoint in G. Since we require a graph to have a nonempty set of vertices, the line graph
is defined only for a graph G that has at least one edge.
The corona of G with H, denoted
, is the graph of order
obtained by taking one copy of G and
copies of H, and joining all the vertices in the ith copy of H to the ith vertex of G. See Figures 6, 7 for a picture of
. Note that
and
are usually not isomorphic (in fact, if
, then
).
Definition 1.1 The j th power of a graph G is a graph with the same set of vertices as G and an edge between two vertices if there is a path of length at most j between them.
Definition 1.2 For such a matrix, the graph of A, denoted
, is the graph with vertices
and edges
. Note that the diagonal of
is ignored in determining
. The set of symmetric matrices of graph G (over R) is defined to be

The minimum rank of a graph G (over R) is defined to be

For
the corank of A is the nullity of A and the maximum nullity (or maximum corank) of a graph G (over R) is defined to be

Clearly

More generally, the minimum rank of a simple graph G is defined to be the smallest possible rank over all symmetric real matrices whose ijth entry (for
) is nonzero whenever
is an edge in G and is zero otherwise [1,2].
The solution to the minimum rank problem is equivalent to the determination of the maximum multiplicity of an eigenvalue among the same family of matrices [3].
2. Zero Forcing Sets and the Graph Parameter 
Here we introduce the graph parameter
as the minimum size of a zero forcing set from [1]. The zero forcing number is a useful tool for determining the minimum rank of structured families of graphs and small graphs [4].
Definition 2.1 Color-change rule:
• If G is a graph with each vertex colored either white or black, u is a black vertex of G, and exactly one neighbor v of u is white, then change the color of v to black.
• Given a coloring of G, the derived coloring is the result of applying the color-change rule until no more changes are possible.
• A zero forcing set for a graph G is a subset of vertices Z such that if initially the vertices in Z are colored black and the remaining vertices are colored white, the derived coloring of G is all black.
•
is the minimum of
over all zero forcing sets
.
For example, an endpoint of a path is a zero forcing set for the path. In a cycle, any set of two adjacent vertices is a zero forcing set.
Corollary 2.2 [1,5] Let
be a graph and let
be a zero forcing set. Then
, and thus
.
The Colin deVerdiere-type parameter
can be useful in computing minimum rank or maximum nullity (over the real numbers). A symmetric real matrix M is said to satisfy the Strong Arnold Hypothesis provided there does not exist a nonzero symmetric matrix X satisfying:
where
denotes the Hadamard (entrywise) product and I is the identity matrix. For a graph G,
is the maximum nullity among matrices
that satisfy the Strong Arnold Hypothesis.
It follows that
.
A contraction of G is obtained by identifying two adjacent vertices of G, and suppressing any loops or multiple edges that arise in this process. A minor of G arises by performing a series of deletions of edges, deletions of isolated vertices, and/or contractions of edges. A graph parameter
is minor monotone if for any minor
of G,
. The parameter
was introduced in [6], where it was shown that
is minor monotone. It was also established that
and
(under the assumptions that
) (see [5,7]).
Corollary 2.3 [2,6] Let G be a graph.
1) If
is a minor of G, then

2) If
,
and
is a minor of G, then

Other possible bounds for minimum rank derived from certain easy to compute parameters of the graph were considered, leading to an investigation of the connection between minimum degree of a vertex,
and minimum rank [8].
Corollary 2.4 For any graph G and infinite field F, 
3. Main Results
In here we calculate the minimum rank of graph
. For this purpose we obtained Zero forcing set and the graph parameter
and the parameter
and determined the upper bound and lower bound for maximum nullity
. Since we have
.
Then we can achieve minimum rank of graphs (see Table 1).
Theorem 3.1 For all
and for all graph such that
, we have 
Proof. It is clear that

and 
then

Proposition 3.2 [1] For each of the following families of graphs,
:
1) Any graph G such that
. (The minimum ranks of all graphs of order at most 7 are available in the spreadsheet [9]).
2) 
3) Any tree T.
4) Some families of special graphs.
By this Proposition we have following Theorem.
Theorem 3.3 For each of the following families of graphs
:
1) Any graph G such that
.
2) Complete graph and complete multipartite graph.
3) Petersen graph.
4) Wheel graph.
5) Clebsch Graph for
.
6) Complement of cycle graph
, where
.
Proof. The proof is trivial.
Proposition 3.4 If
be a path graph,
is homomorphic to the complete graph
.

Table 1. Summary of minimum rank results established in this paper.
Theorem 3.5 For all
we have 
Proof. (Figure 1) In graph
, we have
, because if we start coloring from the end point vertex u, this vertex at least is adjacent with j vertices. The vertex u with its
adjacent vertices are coloring. The other vertices also are coloring since they are adjacent to coloring vertices, and the number of coloring vertices is j, therefore we have
. From Corollary 2.2 we have
.
On the other hand with
contraction of the vertices of
, we reach to the complete graph
, and we know that
is a minor for
. Then we have

Consequently

thus,

(see also [10]).
Proposition 3.6
is homomorphic to
.
Theorem 3.7
, for all
.
Proof. (Figure 2) In
any vertex u is adjacent to 2j vertices, then

On the other hand,
. Then

and finally
Proposition 3.8 For all
we have
.
Theorem 3.9 For all
we have
.
Proof. (Figures 3, 4, 5) In the jth power of graph
an external vertex
is adjacent to
vertices. If we start to coloring of external vertex, then
vertices are coloring, which
colored vertices are in external cycle and the remaining vertices are located in the inner cycle. For more coloring we use the nearest adjacent vertex to
on the set of external vertices. We call this vertex by
adjacent vertices to
are same adjacent vertices to
, and only two of them is different. One of these, which is located on the inner cycle has colored and the another vertex that is located on the external cycle colored from “color-change rule”. We continue the process until all vertices are colored on the internal cycle. Finally

Theorem 3.10 For all
and
we have
.
Proof. (Figures 3, 4, 5) when we make
power of
, then its internal cycle reach to complete graph. With contraction of
vertices to
vertices of this graph we reach to complete graph of order
then
is a minor of
. On the other hand we have
Also according to the previous Theorem, we have
, then

and hence 
Proposition 3.11
is homomorphic to
.
Theorem 3.12 
Proof. (Figures 6, 7) With the contraction of
external vertices of
on the vertices which is located in internal cycle, we have the complete graph
, then the minor of
is
So we have

On the other hand it is trivial that
. And we have
then
Proposition 3.13 [1] If G has
vertices and contains a Hamiltonian path, then
.
Theorem 3.14 For all
and
vertices we have,
.
Proof. By using the induction on the
it can be shown that
contains a Hamiltonian path. So we have,
.