`ISRN AlgebraVolume 2011 (2011), Article ID 193560, 10 pageshttp://dx.doi.org/10.5402/2011/193560`
Research Article

## Graph Polynomials

Department of Mathematics, Iran University of Science and Technology, Narmak, Tehran 16844, Iran

Received 23 May 2011; Accepted 5 July 2011

#### Abstract

One of the most important and applied concepts in graph theory is to find the edge cover, vertex cover, and dominating sets with minimum cardinal also to find independence and matching sets with maximum cardinal and their polynomials. Although there exist some algorithms for finding some of them (Kuhn and Wattenhofer, 2003; and Mihelic and Robic, 2005), but in this paper we want to study all of these concepts from viewpoint linear and binary programming and we compute the coefficients of the polynomials by solving a system of linear equations with variables.

#### 1. Introduction

All graphs in this note are simple, connected, finite, and undirected, though it is probable that some of the obtained results are extendable to general or directed graphs.

Let be a simple and connected graph with and ; then the edge cover and edge dominating polynomials are of degree , and the vertex cover and dominating polynomials are of degree , in which coefficient of is the number of edge cover, edge dominating, vertex cover, and dominating sets with elements, respectively. Also the independence and matching polynomials are at most of degree such that coefficient of is the number of independence and matching sets with elements, respectively, for some positive integer .

For some notation being not defined here we refer the reader to [1].

A set is an edge cover if every vertex is incident to some edge of .

A set is a vertex cover if every edge has at least one endpoint in .

A set is an independent set if non of two vertex in are not adjacent.

The maximum size of an independent set is named independence number.

A matching in graph is a set with no shared endpoints.

In graph a set is a dominating set if every vertex number in has a neighbor in , and finally a set is an edge dominating set if every edge number in has a neighbor in .

We set By [1],(i), (ii).

Also in every bipartite graph(iii), (iv).

We denote the Adjacency matrix by and Incidence matrix by , in which such that and also in which We also define an Edge adjacency matrix as follows: and .

From now on we set

#### 2. Edge Cover Set and Edge Cover Polynomial

As previous notations we have the following theorem for obtaining the minimum size of edge cover set.

Theorem 2.1. One has

Proof. Since an edge cover set of is a set of edges such that every vertex of is incident to some edge of and we want to obtain the optimal size of the sets in covering problems, so we will have a minimize problem; that is, the object function is ; on the other hand for each at least one edge with endpoint must belong to ; in other words from every row of matrix at least one entry must be equal to 1. Therefore

Definition 2.2. An edge cover polynomial is as follows: where is the same as in (2.1) and ’s are the number of edge cover sets with elements.

Theorem 2.3. The coefficients in edge cover polynomial are all of solutions of the following system for , respectively,

Proof. The first inequality is the condition for a set to be an edge cover set and for each causes that we have the edge cover sets with cardinality , respectively, and with this process we can compute . It is trivial that and this completes the proof.

Algorithm 2.4 (For computation ). One has the following.Step 1. Solve and obtain .Step 2. For to , compute all of solutions: Step 3. Set to be equal to all solutions of Step 2.

#### 3. Independence Set and Independence Polynomial

In an independence set from every two adjacent vertices at most one of them belongs to ; this means that for all with end points and at most or belongs to . Therefore we have the following.

Theorem 3.1. One has

Definition 3.2. An independence polynomial is as follows: where is the same as in (3.1) and ’s are the numbers of independence sets with elements.

Theorem 3.3. The coefficients are all of solutions of the following system , respectively,

Algorithm 3.4 (For computation ). One has the following.Step 1. Solve and obtain .Step 2. For to , compute all of solutions: Step 3. Set to be equal to all solutions of Step 2 of course .

#### 4. Vertex Cover Set and Vertex Cover Polynomial

We have the following theorem for vertex cover set.

Theorem 4.1. One has

Proof. Since a vertex cover set of is a set of vertices such that every edge of is incident to some vertex of and we want to obtain the optimal size of the sets in covering problems, so we will have a minimize problem; that is, the object function is ; on the other hand for each with endpoint and at least one of them must belong to ; in other words from every row of matrix at least one entry must be equal to 1. Therefore

Definition 4.2. A vertex cover polynomial is as follows: where is the same as in (4.1) and ’s are the number of vertex cover sets with elements.

Theorem 4.3. The coefficients are all of solutions of the following system for , respectively,

Proof. The first inequality is the condition for a set to be a vertex cover set and for each causes that we have the vertex cover sets with cardinality , respectively, and with this process we can compute . It is trivial that and this completes the proof.

Algorithm 4.4 (For computation ). One has the following.Step 1. Solve and obtain .Step 2. For to , compute all of solutions: Step 3. Set to be equal to all solutions of Step 2.

#### 5. Matching Set and Matching Polynomial

In a matching set from every two adjacent edges at most one of them belongs to and this means that for all with common endpoint at most or belongs to . Therefore we have the following.

Theorem 5.1. One has

Definition 5.2. A matching polynomial is as follows: where is the same as in (5.1) and ’s are the number of matching sets with elements.

Theorem 5.3. The coefficients are all of solutions of the following system, respectively, ,

Algorithm 5.4 (For computation ). One has the following.Step 1. Solve and obtain .Step 2. For to , compute all of solutions: Step 3. Set to be equal to all solutions of Step 2, , of course .

#### 6. Dominating Set and Dominating Polynomial

With the same argument in the previous sections we have the following theorem.

Theorem 6.1. One has

Definition 6.2. A dominating polynomial is as follows: where is the same as in (6.1) and ’s are the number of dominating sets with elements.

Theorem 6.3. The coefficients are all of solutions of the following system, respectively, ,

Proof. The first inequality is the condition for a set to be a dominating set and for each causes that we have the dominating sets with cardinality , respectively, and with this process we can compute . It is trivial that and this completes the proof.

Algorithm 6.4 (For computation ). One has the following.Step 1. Solve and obtain .Step 2. For to , compute all of solutions: Step 3. Set to be equal to all solutions of Step 2.

#### 7. Edge Dominating Set and Edge Dominating Polynomial

With the same argument in previous sections we have Theorems 7.1 and 7.3.

Theorem 7.1. One has

Definition 7.2. An edge dominating polynomial is a polynomial such as where is the same as in (7.1) and ’s are the number of edge dominating sets with elements.

Theorem 7.3. The coefficients are all of solutions of the following system, respectively, ,

Algorithm 7.4 (For computation ). One has the following.Step 1. Solve and obtain .Step 2. For to , compute all of solutions: Step 3. Set to be equal to all solutions of Step 2.

#### References

1. D. B. West, Introduction to Graph Theory, Prentice Hall Inc., Upper Saddle River, NJ, USA, 1996.
2. F. Kuhn and R. Wattenhofer, “Distributed combinatorial optimization,” Tech. Rep. 426, Department of computer Science, ETH Zurich, Zurich, Switzerland, 2003.
3. J. Mihelic and B. Robic, “Solving the k-center problem efficiently with a dominating set,” Algorithm Journal of Computing and Information Technology, vol. 13, no. 3, pp. 225–233, 2005.