Solving the Maximum Matching Problem on Bipartite Star₁₂₃-Free Graphs in Linear Time

Ruzayn Quaddoura^*

Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, Jordan.

DOI: 10.4236/ojdm.2016.61003                

Abstract

The bipartite Star₁₂₃-free graphs were introduced by V. Lozin in [1] to generalize some already known classes of bipartite graphs. In this paper, we extend to bipartite Star₁₂₃-free graphs a linear time algorithm of J. L. Fouquet, V. Giakoumakis and J. M. Vanherpe for finding a maximum matching in bipartite Star₁₂₃, P₇-free graphs presented in [2]. Our algorithm is a solution of Lozin’s conjecture.

Keywords

Bipartite Graphs, Decomposition of Graphs, Design and Analysis of Algorithms, Matching

Received 6 April 2015; accepted 23 January 2016; published 26 January 2016

1. Introduction

A matching M of a graph is a subset of edges with the property that no two edges of M share a common vertex. A matching is called induced if the subgraph of G induced by M consists of exactly M itself. The maximum matching problem is to find a matching with the maximum cardinality. Graph matching is one of the fundamental problems in combinatorial optimization because of its use in various fields such as computational biology [3] , pattern recognition [4] , computer vision [5] , music information retrieval [6] , and computational music theory [7] . For arbitrary graphs, it is known that this problem can be solved in time [8] . Moitra and Johnson gave an time algorithm on interval graphs [9] . In addition Alt, Blum, Mehlhorn,

and Paul gave an time algorithm on bipartite graphs [10] . In [11] Yu and Yang exhibited an

time algorithm for the maximum matching problem on cographs. This result was extended in [12] by Fouquet, Parfenoff and Thuillier to a wider class, namely the -tidy graphs. Also the technique developed in [11] was used by Fouquet, Giakoumakis and Vanherpe in [2] to find an time algorithm for the maximum matching problem on bipartite -free graphs (see Figure 1). In [1] , Lozin studied the class of bipartite

Figure 1. Forbidden configurations for -free graphs.

-free graphs and conjectured that both maximum induced matching problem and maximum matching problem in this class can be solved in linear time. The first one has been solved in [13] . In this paper we shall solve Lozin’s conjecture for maximum matching problem by extending the matching algorithm for the class of -free graphs [2] to the class of bipartite -free graphs. Our algorithm is based on the recognition algorithm of the class -free bipartite graphs introduced by Quaddoura in [14] .

2. Definitions and Properties

For terms not defined in the paper the reader can refer to [15] . The graphs considered in this paper are finite without multiple edges and loops. As usual, for any graph G we denote the set of its vertices by and by the set of its edges (or simply by V and E if there is no risk of confusion) and their respective cardinalities by n and m. A bipartite graph is defined by two disjoint vertex subsets B the black vertices and W the white ones, and a set of edges. The bi-complement of a bipartite graph is the bipartite graph defined by. If the color classes B and W are both non empty, the graph will be called bichromatic, monochromatic otherwise. A vertex x will be called isolated (resp. universal) if x has no neighbors in G (resp. in). A complete bipartite graph is a graph having only universal white vertices and universal black vertices. A stable set is a subset of pairwise non-adjacent vertices. A chordless path on k vertices is denoted by and a chordless cycle on k vertices is denoted by. Given a subset X of the vertex set, the subgraph induced by X will be denoted by. A set is called a module if every vertex in is either adjacent to all vertices in A or none of them. The representative graph of a graph G is the subgraph of G induced by the set of vertices containing one vertex from each proper maximal module of G. A graph G is called Z-free where Z is a set of graphs, when G does not contain an induced subgraph isomorphic to a graph of Z.

Definition 1 [2] . Given a bipartite graph of order at least 2, G is graph if and only if G contains an isolated vertex or its vertex set can be decomposed into two sets K and S such that K induces a complete bipartite graph while S is a stable set.

Property 2 [2] . Let be a bipartite graph of order at least 2. G is graph if and only if there exists a partition of its vertex set into two non empty classes and such that all possible edges exists between the black vertices of and the white vertices of while there is no edge connecting a white vertex of with a black vertex of.

Such partition is referred as associated partition of G and is denoted by the ordered pair (,) [2] .

Property 3 [2] . A bipartite graph G is a graph if and only if G admit a unique (up to isomorphism) partition of its vertex set satisfying the following conditions:

1) is an associated partition to the graph G.

2) is not a graph.

The partition of the above property is called decomposition while a set said to be component of the graph.

From decomposition together with the decomposition of bipartite graph G into its connected components (parallel decomposition) or those of (series decomposition) yield a new decomposition scheme for G called canonical decomposition. It is shown in [2] that whatever the order in which the decomposition operators are applied (decomposition, series decomposition or parallel decomposition), a unique set of inde- composable (or prime) graphs with respect to canonical decomposition is obtained. Obviously, a unique tree is

associated to this decomposition. The internal nodes are labeled according to the type of decomposition applied, while every leaf correspond to a vertex of G. Hence there are four types of internal nodes, parallel node (labeled P), series node (labeled S), node (labeled), and indecomposable node (labeled N). By convention, the set of vertices corresponding to the set of leafs having an internal node as their least common ancestor will be denoted simply by.

Lozin in [1] gives the following characterization for bipartite Star₁₂₃-free graphs.

Theorem 4 [1] . Let G be a bipartite Star₁₂₃-free graph. One of the following hold.

1) G is graph.

2) G and aren’t both connected.

3) The representative graph of G or the bi-complement of the representative graph of G is a path or a cycle with.

It is shown in [14] that the representative graph of a graph G is a path or or a cycle or if and only if G is an extended path or a bi-complement of an extended path or an extended cycle or a bi-complement of an extended cycle respectively. More precisely, (see Figure 2).

Definition 5 [14] . A graph G is said to be an extended path if there is a partition of the vertex set of

G into a monochromatic sets such that.

Definition 6 [14] . A graph G is said to be an extended cycle if there is a partition of the vertex set of

G into a monochromatic sets such that.

The construction of the canonical decomposition tree of a bipartite Star₁₂₃-free graph can be obtained in linear time from the algorithm given by Quaddoura in [14] . According to this algorithm, every child of a N-node is a node marked by corresponding to a set, if, or to a vertex of G otherwise. Figure 3 illustrates a bipartite Star₁₂₃-free graph and its canonical decomposition tree.

3. Maximum Matching of Bipartite Star₁₂₃-Free Graphs

In this section we will extend the techniques developed in [2] to provide an time algorithm for the maximum matching problem on bipartite Star₁₂₃-free graph. We present first the required tools for this purpose.

A classical tool for solving the maximum matching problem was introduced by Berge in [16] . Let M be any matching of a graph, an M-alternating path is a path whose edges are alternately in M and in. If some edge of M is incident to a vertex v, this vertex is said to be saturated by M, otherwise v is M-unsaturated. An M-augmenting path is an M-alternating path whose both endpoints are M-unsaturated.

Figure 2. An and an.

Figure 3. A bipartite Star₁₂₃-free graph and its canonical decomposition tree.

Theorem 7 [16] . A matching M of a graph G is maximum matching if and only if G contain no M-aug- menting path.

Consider a bipartite graph G such that G admit a decomposition according to some rule into two graphs and. Let and be maximum matchings of and, let which is a matching of G. In order to increase the size of M we use the operations Match and Split (see [11] ) described below.

Let be the set of -unsaturated vertices of and be the set of -unsatureted vertices of. A Match operation occurs if there are two adjacent vertices and then the edge is added to M, the vertices and are thus saturated by M and they are respectively deleted from the sets and.

Let U be the set of M-unsaturated vertices, a Split operation occurs if there exists an edge of M say xy and vertices u and v belonging to U such that u is adjacent to x and v is adjacent to y. In that case the Split operation constructs a new matching defined by, the vertices u and v being saturated by and deleted from U and the edge xy is deleted from M. Note that, if is a bipartite complete then a maximum matching of G can be obtained by applying Match operations between the two sets B and W.

Let now G be a bipartite Star₁₂₃-free graph and is its canonical decomposition tree. For our purpose we shall modify to a binary tree as follows: We visit all nodes of in DFS order. For a node of type P, S or let be the children of. If then does not change. Else remains its left child and is its new child labeled by P, S or respectively with are its children. For a N-node, using the Procedure MAXMATCH, the Procedure MAXMATCH, or the procedure MAXMATCH described below, which find a maximum matching of an extended path or an extended cycle or their bi-complements, we replace by a leaf together with a maximum matching of the subgraph and the set of unsatureted vertices with respect to this matching. Our algorithm uses post order traversal to visit all the nodes of. Whenever an internal node of this binary tree is visited, we compute a maximum matching of from the maximum matching of and of where and are the two children of in. For this purpose we distinguish the following cases according to the type of.

3.1. α Is of Type P, S or K + S

Consider now the set which is a matching of. Obviously, if is a P-node then M is a maximum matching of. In the case when is of type S or K + S we use the same technique used in [6] to find a maximum matching of.

Let be the set of -unsaturated vertices of and be the set of -unsatureted vertices of. Let be the matching of obtained when all possible Match operations have been sequentially performed.

Let now U be the set of M-unsaturated vertices, and Let be the matching obtained when all possible Split operations have been sequentially performed.

Theorem 8 [2] . If is a K + S-node, the set is a maximum matching of.

Theorem 9 [2] . Assume that is a S-node and M is equal to. Let U be the set of M-un- saturated vertices of, then the set is a maximum matching of.

3.2. α Is of Type N

In this section we will develop an algorithm to find a maximum matching of an extended path or an extended cycle and an algorithm to find a maximum matching of their bi-complement (see Definitions 5 and 6). We can suppose that if k is even or if k is odd. We denote by the matching of the bipartite complete graph obtained by Match operations between the two monochromatic sets and. When an edge xy is added to this matching where and then x will be deleted from and y will be deleted from. Note that, during the execution of Procedure MAXMATCH or the Procedures MAXMATCH and MAXMATCH, the matching is not necessarily maximum for, this is because some vertices of or may already be saturated.

3.2.1. α Is EP_k or EC_k

Procedure MAXMATCH provides a maximum matching of an extended path or an extended cycle. By convention, every monochromatic set of an extended path or an extended cycle has an odd index consists of black vertices and those having an even index consist of white ones. For the purpose of simplification, the length k of the extended path in this Procedure is considered to be odd, if this length is even then the set is considered to be empty.

Procedure MAXMATCH EP_k, EC_k

1)

2) if then

3) for to do

begin for

4)

5)

end for

Theorem 10. Let be an extended path or an extended cycle where . Procedure MAXMATCH produces a maximum matching of G.

Proof. Let be an M-augmenting path in G. Since G is a bipartite, t is even, so and are of different colors. Without loss of generality, assume that is a black vertex and is white. Let and.

Claim 1. There is no black vertex of P in.

Proof. Let be the first black vertex of P in, then must be in. Since is a black vertex non saturated, it must be. According to our Procedure, the edge has been added to M by the operation, but before this step, the edge must be added to M by the step, a contradiction. ■

Claim 2. There is no white vertex of P in.

Proof. Let be the last white vertex of P in, then must be in or in or in and. The vertex does not belong to, otherwise, since, the path must contain a vertex in, a contradiction with our choice of. If then according to our Procedure, the edge has been added to M by the operation when, but in the step, the edge must be added to M by the operation, a contradiction. If then and. In this case, the vertex does not belong to or to, otherwise must be added to M, so. But now the set must contain a vertex of P, a contradiction with Claim 1. ■

Suppose that G is an or G is an such that there is no edge of P connecting and. If then either contains a vertex of P or there is an edge of P connecting and, a contradiction. If then either contains a vertex of P or there is an edge of P connecting and, a contradiction. Therefore. But now the edge must be added to M by the operation, a contradiction.

Suppose now G is an and there is an edge of P connecting and. Let be the first edge of P connecting and. By Claim 1 and Claim 2,. Thus does not belong to. If then according to our choice of, the set must contain a vertex of P, a contradiction with Claim 1. Therefore. Since the vertex is a black non-saturated vertex, the edge does not belong to M, By our choice of the edge, the vertex must belong to. Now, the edge has been added to M by the operation. But before this step, the edge must be added to M by the operation, a contradiction. □

The following Table 1 illustrates a trace of the Procedure MAXMATCH for the in Figure 2 where a vertex in is denoted by.

3.2.2. α Is or

Note that the matching obtained by the Procedure MAXMATCH is ensured to be maximum because of the order of applying Match operations. In the case when is or, we must also design an order of applying Match operations and Split operations to ensure that the resulting matching is maximum. For this purpose, we will study first the structure of a M-augmenting path of a matching M of obtained by doing in an arbitrary order all possible Match operations then all possible Split operations (Lemma 12 and Lemma 13). Knowing this structure will enable us to design an order of applying Match operations (Procedure Match (G)) then developing a Procedure of a maximum matching M for or.

Recall that when an edge xy is added to a matching M by a Match operation where and then x will be deleted from and y will be deleted from. In addition, we suppose here that Match operation associates labels with x and y as and when and respectively. Two monochromatic sets and of different color are called independent if form a stable set, non-independent otherwise.

Lemma 11. Let or, let M be a matching of G obtained when all possible Match operations have been performed. If there are at least two M-unsaturated vertices of different color then all the M-unsaturated vertices are located in at most three consecutive monochromatic sets and where.

Table 1. Illustration of procedure MAXMATCH for the in Figure 2.

Proof. By the hypothesis of the Lemma, all the M-unsaturated vertices must be in independent sets. Obviously any three consecutive sets are independent and the maximum number of independent sets is three. □

Assume that or and M is a matching of G obtained when all possible Match operations have been performed (in an arbitrary order). The following Procedure determines the sets which are the possible location of M-unsaturated vertices. Note that when, the sets and are consecutive.

Procedure M-unsaturated vertices (G, M)

1) Find the small index for which

2) if there is no such s then return M is maximum

else

3) if or then

//when and, does not exist, when and,

4) if then return and

5) else return

else // and

6) if then return

7) else if then return

8) else return

According to Lemma 11, one of the two M-unsaturated vertices of any M-augmenting path in G is in and the second in, or one in and the second in. Consider first a M-augmenting path in G which its M-unsaturated vertices are in and in.

To augment the size of M, Split operations can be done between the M-unsaturated vertices of, the M-unsaturated vertices of and the edges of M whose extremities belong to monochromatic sets non-inde- pendent of and, namely the edges of M whose extremities don’t belong to when and, and the edges of M whose extremities don’t belong to and otherwise. The following Procedure performs these Split operations.

Procedure Split (M, V_s, V_s+1)

1) if and then

2) else

3) while and and do

Begin while

4) let

5)

// assuming that u and x also v and y are of different color

end while

The following Lemma describes the structure of a M-augmenting path whose extremities belong to and.

Lemma 12. After the execution of Procedure Split if there is a M-augmenting path in G whose extremities in and then:

・ or.

・ P can be reduced to a M-augmenting path where, is any non-independent set of and is any non-independent set of.

Proof. Let be a M-augmenting path in G where and. Since after the execution of Procedure Split, and, the set must be empty. Therefore, if and, every edge of M has an extremity in or, and if or, every edge of M has an extremity in or. Obviously, the color of every vertex of P having an odd index (resp. even index) is as the color of (resp.).

Let be the first vertex of P having an odd index and belongs to a set distinct of, then. Since and, either or. If then, a contradiction with our choice of, therefore. Since does not exist when and, then or. Since,. Now the subpath of P can be reduced to.

Let be the last vertex of P having an even index and belongs to a set distinct of, then. Since and, either or. If then, a contradiction with our choice of, therefore. Since and,. Obviously. Since,. Now the path P can be reduced to the M-augmenting path. □

Consider now a M-augmenting path in G such that its M-unsaturated vertices are in and in. In a similar way, by replacing in the above Procedure and in Lemma 12, by s, s by, by and by, we obtain the Procedure Split and Lemma 13which describes the structure of a M-augmenting path whose extremities belong to and.

Procedure Split (M, V_s+1, V_s+2)

1) if and then

2) else

3) while and and do

begin while

4) let

5)

//assuming that u and x also v and y are of different color

end while

Lemma 13. After the execution of Procedure Split if there is a M-augmenting path in G whose extremities in and then:

・ or.

・ P can be reduced to a M-augmenting path where, is any non-independent set of and is any non-independent set of.

We start now by developing a Procedure for a maximum matching in or. The order of applying Match operations is defined in following Procedure which called MATCH (G). Recall that either or.

Procedure MATCH (G)

1)

2)

3) For to (or to if) do

begin for

4)

5) while and do

begin while

6)

7) if then

end while

8)

9)

10) while and do

begin while

11)

12) if then

end while

13)

end for

Procedure MATCH (G) works as following, for every to (or to if):

・ Add to M the possible edges between as long as it is non empty and (the non-independent sets of having indices less than) with respect to this order, where l determines the last non empty set in during the for loop iterations.

・ Add to M the possible edges between as long as it is non empty and (the non-independent sets of having indices less than 2i) with respect to this order, where h determines the last non empty set in during the for loop iterations.

Observation 14. According to Procedure MATCH (G):

・ if is an edge of M created by the operation then.

・ if (resp.) then the edges of have been added to M before adding the edges of (resp.).

・ if then the edges of have been added to M before adding the edges of.

The following Table 2 illustrates a trace of the Procedure MATCH (G) for the in Figure 2. The second and the third column of this table represent the execution of steps 5 and 10 respectively.

The combination of Procedures MATCH (G), M-unsaturated vertices, Split, and Split

provides the Procedure MAXMMATCH. For a maximum matching of we need

a little addition. Theorem 15 proves their correctness.

Procedure MAXMATCH

1) MATCH

2) M-unsaturated vertices

3) Split

4) Split

Procedure MAXMATCH

1) MATCH

2) M-unsaturated vertices

3) Split

4) Split

5) if and then

//Assuming that x and the vertices of also y and the vertices of are of the same color

6)

7)

8) while and and and do

begin while

let

9)

10)

end while

Theorem 15. Procedure MAXMATCH and Procedure MAXMATCH produce a maximum matching of and respectively.

Proof. Suppose that after execution of Procedure MAXMATCH or Procedure MAXMATCH there is a M-augmenting path. Since and are of different color and all the monochromatic sets are empty except at most and, there are two cases, either or .

Let when or when. By Lemma 12 and Lemma 13, P can be reduced to a path where, is any non-independent set of and is any non-independent set of. Assume first that and.

Claim 1. The edge is obtained by Split operation.

Proof. Suppose that the edge is obtained by Match operation. Since the edge is obtained either by or by. Without loss of generality, assume that is ob-

Table 2. Illustration of Procedure MATCH (G) for the in Figure 2.

tained by. By Observation 14,. Since,. So the operation

exists and must precedes the operation by Observation 14 and since. Therefore, the

edge must be added to M instead of adding the edge, a contradiction. ■

Claim 2..

Proof. If then. By Claim 1, is obtained by Split operation, thus the vertex must belong to, a contradiction since and are independent. ■

Since, the edge is obtained by the step 4, that is by Split. Let be the edge which was in M and which has been used in step 4 of the Procedure Split to obtain the edge. The vertex must be identical to x or to y. Let x be the vertex. By the definition of, the vertices x and y don’t belong to. Since and,. Obviously,. Therefore where is the set defined in step 4 of the Procedure Split. But before executing the step Split, the set must be empty since, a contradiction.

Assume that. Then and. By Lemma 12 and Lemma 13, , , is any non-independent set of and is any non-in- dependent set of. Since then and. Therefore one of the sets and in step 8 must be empty. This is contradicted with the fact that and.

Assume finally that. Then and. Since in this case and must be independent,. By Lemma 12 and Lemma 13, , is any non-independent set of and is any non-independent set of.

Claim 3. The edge is obtained by Split operation.

Proof. Suppose that the edge is obtained by Match operation. Since, and, then the edge is obtained by. By Observation 14, since, the operation exists and must precede the operation. Therefore, the edge must be added to M instead of adding the edge, a contradiction. ■

Claim 4..

Proof. If then. By Claim 3, is obtained by Split operation, thus the vertex must belong to, a contradiction since and are independent. ■

Since, the edge is obtained by the step 3, that is by Split. Let be the edge which was in M and which has been used in step 4 of the Procedure Split to obtain the edge. The vertex must be identical to x or to y. Let x be the vertex and let. By the definition of,. Obviously, was not created by split operation. By Observation 14, if (resp.)

then was created by (resp.). Since (resp.), the operation

precedes the operation (resp.). So the edge must be added to M instead of adding, a contradiction. □

Lets apply the Procedure MAXMATCH on the graph in Figure 2. As we shown above,

Procedure MATCH (G) produces the matching. Procedure M-unsa-

turated vertices gives that and does not exist. Procedure Split

gives that and.

Since does not exist, Procedure Split gives nothing.

3.3. The Whole Algorithm

Let us present now our algorithm for the maximum matching problem on bipartite -free graphs. Theorems 8, 9, 10, and 15 prove its correctness.

Algorithm Maximum Matching

Input: A bipartite -free graph G and its binary canonical decomposition tree.

Output: M a maximum matching of G and U the set of M-unsaturated vertices of G.

1) Let be a node on a postorder traversal of.

2) If is a leaf or is a -node then.

3) Else if is a N-node then.

4) If or then, -unsaturated

vertices.

5) Else if then, -unsaturated vertices.

6) Else, -unsaturated vertices.

7) Replace by a leaf together with M and U.

8) Else let and be the two children of in.

9) Let and be respectively the maximum matchings and.

10) and be respectively the sets of unsaturated vertices of and.

11) If is a P-node then.

12) Else if is a -node then, -unsaturated vertices.

13) Else, -unsaturated vertices,

-unsaturated vertices.

3.4. Complexity

We show now that the complexity of our algorithm is.

The total number of Match operations performed by, is at most. So the run time of step 4 (Procedure) is.

Consider the steps 5 and 6 which are the Procedures MAXMATCH and MAXMATCH. The variables l and h in Procedure MATCH (G) assure that the sum of iterations of all while loops in this Procedure

is. Since and the number of Match operation performed by is at most

then MATCH (G) runs in time. The Procedure M-unsaturated vertices runs in time since.

Since the size of the matching obtained by MATCH (G) is less than or equal to, the construction of the set and defined in Procedures Split, Split and MAXMATCH, as well as the while loops defined in these Procedures costs time. So steps 5 and 6 runs in time.

The total number of Match or Split operations performed in steps 8 to 13 is bounded by the size of maximum matching obtained, which is less or equal to ( [2] ), so the run time of steps 8 to 13 is.

Finally, since the number of visited nodes in is, this algorithms runs with time complexity.

4. Conclusion

The maximum matching is computed in time, given a binary canonical decomposition tree of a bipartite -free graph. The canonical decomposition of a bipartite -free graph can be done in time [14] including the binary canonical decomposition tree construction. Thus, the whole process is in time.

Acknowledgements

This research is funded by the Deanship of Research and Graduate Studies in Zarqa University/Jordan. The author is grateful to anonymous referee’s suggestion and improvement of the presentation of this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Lozin, V.V. (2002) Bipartite Graphs without a Skew Star. Discrete Mathematics, 257, 83-100. http://dx.doi.org/10.1016/S0012-365X(01)00471-X
[2]	Fouquet, J.L., Giakoumakis, V. and Vanherpe, J.M. (1999) Bipartite Graphs Totally Decomposable by Canonical Decomposition. International Journal of Foundation of Computer Science, 10, 513-533. http://dx.doi.org/10.1142/S0129054199000368
[3]	Ben-Dor, A., Karp, R.M., Schwikowski, B. and Shamir, R. (2003) The Restriction Scaf-Fold Problem. Journal of Computational Biology, 10, 385-398. http://dx.doi.org/10.1089/10665270360688084
[4]	Buss, S.R. and Yianilos, P.N. (1995) A Bipartite Matching Approach to Approximate String Comparison an Search. Technical Report, NEC Research Institute, Princeton.
[5]	Demirci, M.F., Shokoufandeh, A., Keselman, Y., Bretzner, L. and Dickinson, S. (2006) Object Recognition as Many-to-Many Feature Matching. International Journal of Computer Vision, 69, 203-222. http://dx.doi.org/10.1007/s11263-006-6993-y
[6]	Toussaint, G.T. (2004) A Comparison of Rhythmic Similarity Measures. 5th International Conference on Music Information Retrieval, 242-245.
[7]	Toussaint, G.T. (2005) The Geometry of Musical Rhythm. Japan Conference on Discrete and Computational Geometry, Berlin-Heidelberg, 198-212. http://dx.doi.org/10.1007/11589440_20
[8]	Micali and Vazirani, V.V. (1980) An O(m√n) Algorithm for Finding Maximum Matching in General Graphs. FOCS, 17-27.
[9]	Moitra and Johnson, R.C. (1989) A Parallel Algorithm for Maximum Matching in Interval Graphs. Proceedings of International Conference on Parallel Processing, 3, 114-120.
[10]	Alt, H., Blum, N., Mehlhborn, K. and Paul, M. (1991) Computing a Maximum Cardinality Matching in Bipartite Graphs in Time O(n1.5√m log n) . Information Processing Letters, 37, 237-240. http://dx.doi.org/10.1016/0020-0190(91)90195-N
[11]	Yu, M.S. and Yang, C.H. (1993) An O(n) Time Algorithm for Maximum Matching on Cographs. Information Processing Letters, 47, 89-93. http://dx.doi.org/10.1016/0020-0190(93)90230-7
[12]	Fouquet, J.L., Parfenoff, I. and Thuillier, H. (1997) An O(n) Time Algorithm for Maximum Matching in P4-Tidy Graphs. Information Processing Letters, 62, 281-287. http://dx.doi.org/10.1016/S0020-0190(97)00081-1
[13]	Quaddoura, R. (2014) An O(n) Time Algorithm for Maximum Induced Matching In Bipartite Star123-Free Graphs. World of Computer Science and Information Technology Journal (WCSIT), 4, 38-41.
[14]	Quaddoura, R. (2006) Linear Time Recognition Algorithm of Bipartite Star123-Free Graphs. International Arab Journal of Information Technolog, 3, 193-202.
[15]	Bondy, J.A. and Murty, U.S.R. (1979) Graph Theory with Applications. North Holland, New York.
[16]	Berge, C. (1957) Two Theorems in Graph Theory. Proceedings of the National Academy of Sciences of the United States of America, 43, 842-844. http://dx.doi.org/10.1073/pnas.43.9.842