Graph theory

CHAPTER 1
Basic Concepts and
Definitions of Graph Theory
1.1 INTRODUCTION
Graph theory is a branch of mathematics started by Euler [45] as early as 1736. It
took a hundred years before the second important contribution of Kirchhoff [139]
had been made for the analysis of electrical networks. Cayley [22] and Sylvester
[228] discovered several properties of special types of graphs known as trees.
Poincaré [195] defined in principle what is known nowadays as the incidence
matrix of a graph. It took another century before the first book was published by
König [141]. After the second world war, further books appeared on graph theory,
Ore [183], Behzad and Chartrand [11], Tutte [240], Berge [13], Harary [70],
Gould [63], and West [245], among many others.
Graph Theory has found many applications in engineering and science, such as
chemical, civil, electrical and mechanical engineering, architecture, management
and control, communication, operational research, sparse matrix technology,
combinatorial optimisation, and computer science. Therefore many books have
been published on applied graph theory such as those by Bondy and Murty [16],
Chen [24], Thulasiraman and Swamy [235], Wilson and Beineke [252], Mayeda
[170], Christofides [26], Gondran and Minoux [61], Beineke and Wilson [12],
Deo [37], Cooke et al. [32], Kaveh [110-111] and many others. In recent years,
due to the extension of the concepts and applications of the graph theory, many
journals such as Journal of Graph Theory, Journal of Combinatorial Theory A &
B, Discrete and Applied Mathematics, SIAM Journal of Discrete Mathematics,
European Journal of Combinatorics, and Graphs and Combinatorics are being
published to cover the advances made in this field.

2 Structural Mechanics: Graph and Matrix Methods
2
In this chapter basic definitions and concepts of graph theory are presented;
however, for proofs and details the reader may refer to textbooks on this subject,
Refs [63,70,245].
1.2 BASIC DEFINITIONS
There are many physical systems whose performance depends not only on the
characteristics of their components, but also on their relative location. As an
example, in a structure, if the properties of a member are altered, the overall
behaviour of the structure will be changed. This indicates that the performance of
a structure depends on the characteristics of its members. On the other hand, if the
location of a member is changed, the properties of the structure will again be
different. Therefore the connectivity (topology) of the structure influences the
performance of the whole structure. Hence it is important to represent a system so
that its topology can be understood clearly. The graph model of a system provides
a powerful means for this purpose.
1.2.1 DEFINITION OF A GRAPH
A graph S consists of a set N(S) of elements called nodes (vertices or points) and
a set M(S) of elements called members (edges or arcs) together with a relation of
incidence which associates each member with a pair of nodes, called its ends.
Two or more members joining the same pair of is are known as a multiple
member, and a member joining a node to itself is called a loop. A graph with no
loops and multiple members is called a simple graph. If N(S) and M(S) are
countable sets, then the corresponding graph S is finite. Since the great majority of
the results in this book pertain to finite graphs with no loop and multiple members,
only simple finite graphs are needed, which are referred to as graphs.
The above definitions correspond to abstract graphs; however, a graph may be
visualized as a set of points connected by line segments in Euclidean space; the
points are identified with nodes, and the line segments without their end points are
identified with members. Such a configuration is known as a topological graph.
These definitions are illustrated in Figure 1.1.
loopmultiple members
(a) A non-simple graph. (b) A simple graph.
Fig. 1.1 Non-simple and simple graphs.

CHAPTER 1 Basic Concepts and Definitions 3
3
1.2.2 ADJACENCY AND INCIDENCE
Two nodes of a graph are called adjacent if these nodes are the end nodes of a
member. A member is called incident with a node if it is an end node of that
member. Two members are called incident if they have a common end node. The
degree (valency) of a node ni of a graph, denoted by deg (ni), is the number of
members incident with that node. Since each member has two end nodes, the sum
of node-degrees of a graph is twice the number of its members (handshaking
lemma - known as the first theorem of graph theory).
n n
nnn m
m
m
m m m
1 2 3
4 5
1
3 4 5 6
7
2m
Fig. 1.2 A simple graph S.
As an example, in Figure 1.2 two nodes n4 and n5 are adjacent. Node n3 is incident
with member m2 and m6, and deg (n2) = 4.
1.2.3 ISOMORPHIC GRAPHS
Two graphs S1 and S2 are called isomorphic if there exists a one-to-one
correspondence between their node sets and adjacency is preserved. As an
example, the three graphs shown in Figure 1.3 are isomorphic. The word
isomorphic is derived from the Greek words same and form.
n n n
nnn
n
n
n
n
n
n
n
n n
n
4 5 6
321
5
32
6
1
4
3
4
6
1
n n 25
(a) (b) (c)
Fig. 1.3 Three isomorphic graphs.

4
1.2.4 GRAPH OPERATIONS
A subgraph Si of S is a graph for which N(Si) ⊆ N(S) and M(Si) ⊆ M(S), and each
member of Si has the same ends as in S.
The union of subgraphs S1, S2 , ... , Sk of S , denoted by Sk
= i
k
1i
S
=
∪ = S1 ∪ S2 ∪ ...
∪ Sk, is a subgraph of S with N(Sk
) = )S(N i
k
1i=
∪ and M(Sk
) = )S(M i
k
1i=
∪ . The
intersection of two subgraphs Si and Sj is similarly defined using intersections of
node-sets and member-sets of the two subgraphs. The ring sum of two subgraphs
Si ⊕ Sj = Si ∪ Sj − Si ∩ Sj is a subgraph which contains the nodes and members of
Si and Sj except those elements common to Si and Sj. These definitions are
illustrated in Figure 1.4.
(a) S (b) Si (c) Sj
(d) Si ∪ Sj (e) Si ∩ Sj (f) Si ⊕ Sj
Fig. 1.4 A graph, two of its subgraphs, union, intersection and ring sum.
There are many other useful operations, such as Cartesian product, direct product
and strong Cartesian product, successfully applied to structural engineering [129].
1.2.5 WALKS, TRAILS AND PATHS
A walk w of S, is a finite sequence w = {n0 , m1 , n1 ,..., mk , nk} whose terms are
alternately nodes ni and members mi of S for 1 ≤ i ≤ k, and ni-1 and ni are the two
ends of mi. A trail t in S, is a walk in which no member of S appears more than
once. A path P in S, is a trail in which no node appears more than once. The
length of a path Pi denoted by L(Pi) is taken as the number of its members. Pi is
called the shortest path between the two nodes n0 and nk, if for any other path Pj

5
between these nodes L(Pi) ≤ L(Pj). The distance between two nodes of a graph is
defined as the number of its members of a shortest path between these nodes.
As an example, in Figure 1.5,
w = ( n1 , m5 , n5 , m6 , n2 , m7 , n6 , m11 , n5 , m6 , n2 , m2 , n3 )
is a walk between n1 and n3, in which member m6 and nodes n2 and n5 are repeated
twice.
n n n
n n nm
m m
m
m
mm
m
m
m
m 9
4 5 6
321
10 11
2
7
6
1
3
5
4 8
(a) A walk w in S. (b) A trail t in S. (c) A path P in S.
Fig. 1.5 A walk, a trail and a path in S.
t = ( n1 , m5 , n5 , m6 , n2 , m7 , n6 , m11 , n5 , m10 , n4 )
is a trail between n1 and n4 in which node n5 is repeated twice.
P = ( n1 , m5 , n5 , m6 , n2 , m7 , n6 )
is a path of length 3 in which no node and no member is repeated.
1.2.6 CONNECTEDNESS
Two nodes ni and nj are said to be connected in S if there exists a path between
these nodes. A graph S is called connected if all pairs of its nodes are connected.
A component of a graph S is a maximal connected subgraph, i.e. it is not a
subgraph of any other connected subgraph of S. These definitions are illustrated in
Figure 1.6.
(a) A connected graph. (b) A disconnected graph.
Fig. 1.6 A connected graph and a disconnected graph with 3 components.

6
1.2.7 CYCLES AND CUTSETS
A cycle is a path (n0 , m1 , n1 , ... , mp , np) for which n0 = np and p ≥ 1 ; i.e. a cycle
is a closed path. Similarly, a closed trail (hinged cycle) and a closed walk can be
defined, Figure 1.7.
(a) A cycle of S. (b) A hinged cycle of S.
Fig. 1.7 Two cycles of S.
A cutset in a graph S is a set of members whose removal from the graph increases
the number of connected components of S, Figure 1.8(a). If a cutset results in two
components S1 and S2, then it is known as prime cutset, Figure 1.8(b). A link is a
member with its ends in two components produced by a cutset. Links are shown in
bold lines. In a prime cutset, if one of the components S1 or S2 consists of a single
node, then the prime cutset is called a cocycle, Figure 1.8(c).
(a) A cutset of S. (b) A prime cutset of S. (c) A cocycle of S.
Fig. 1.8 Different cutsets of S.
1.2.8 TREES, SPANNING TREES AND SHORTEST ROUTE TREES
A tree T of S is a connected subgraph of S, which contains no cycle. A set of trees
of S forms a forest. If a tree contains all the nodes of S, it is called a spanning tree
of S. For simplicity it will be referred to as a tree, from now on.
A shortest route tree (SRT) rooted at a specified node n0 of S, is a tree for which
the distance between every node nj of T and n0 is a minimum. An SRT of a graph
can be generated by the following simple algorithm:
Label the selected root as "0" and the adjacent nodes as "1". Record the members
incident to "0" as tree members. Repeat the process of labelling with "2" the
unnumbered ends of all the members incident with nodes labelled as "1", again

7
recording the tree members. This process terminates when each node of S is
labelled and all the tree members are recorded. The label of the last node indicates
the length of the SRT and the maximum number of nodes with the same label is
defined as the width of the SRT.
The above definitions are illustrated in Figure 1.9. The length and width of the
SRT in Figure 9(d) are 2 and 3, respectively.
It is easy to prove that for a tree T,
M(T) = N(T) − 1, (1-1)
where M(T) and N(T) are the numbers of members and nodes of T, respectively.
The complement of T in S is called a cotree, denoted by T*. The members of T are
known as branches and those of T* are called chords. For a connected graph S,
the number of chords is given by:
M(T*) = M(S) − M(T). (1-2)
Since, N(T) = N(S), hence,
M(T*) = M(S) − N(S) + 1, (1-3)
where M(S) and N(S) are the numbers of members and nodes of S, respectively.
Notice that for a set and its cardinality the same notation is used and the difference
should be obvious from the context.
(a) A graph S. (b) A tree of S. (c) A spanning tree of S.
n0
(d) An SRT rooted from n0. (e) A cotree shown in dashed lines.
Fig. 1.9 A tree and a cotree of S.

8
1.3 DIFFERENT TYPES OF GRAPHS
In order to simplify the study of properties of graphs, different types of graphs
have been defined. Some important ones are as follows:
A null graph is a graph which contains no members. Thus Nk is a graph containing
k isolated nodes.
A cycle graph is a graph consisting of a single cycle. Therefore Ck is a polygon
with k members.
A path graph is a graph consisting of a single path. Hence Pk is a path with k
nodes and (k−1) members.
A complete graph is a graph in which every two distinct nodes are connected by
exactly one member, Figure 1.10.
K1 K2 K3 K4 K5
Fig. 1.10 Some complete graphs.
A complete graph with N nodes is denoted by KN. It is easy to prove that a
complete graph with N nodes has N(N − 1)/2 members.
A graph is called bipartite, if the corresponding node set can be split into two sets
N1 and N2 in such a way that each member of S joins a node of N1 to a node of N2.
A complete bipartite graph is a bipartite graph in which each node N1 is joined to
each node of N2 by exactly one member. If the number of nodes in N1 and N2 are
denoted by r and s, respectively, then a complete bipartite graph is denoted by Kr,s.
Examples of bipartite and complete bipartite graphs are shown in Figure 1.11.
(a) A bipartite graph. (b) A complete bipartite graph K3,4.
Fig. 1.11 Two bipartite graphs.

9
1.4 VECTOR SPACES ASSOCIATED WITH GRAPHS
In this section, it is shown that a vector space can be associated with a graph, and
the properties of two important subspaces of this vector space, namely cycle and
cutset spaces, is studied. For this purpose, simple definitions from sets, groups,
fields and vector spaces are briefly presented. The material presented in this
section is based on the work of Thulasiraman and Swamy [235].
1.4.1 GROUPS AND FIELDS
Consider a finite set S = {a,b,c,….}, and define a binary operation + on S. This
operation assigns to every pair (a and b)∈S a unique element denoted by a+b. The
set S is said to be closed under + if the element (a+b)∈S, whenever (a and b)∈S.
The operation + is said to be associative if a+(b+c) = (a+b)+c for all a, b and c in
S. The operation is called commutative if a+b = b+a for all a and b in S.
Definition 1: A set S with a binary operation +, called addition, is a group if the
following postulates hold:
1. S is closed under +.
2. The operation + is associative.
3. There exists a unique element e∈S such that a+e = e+a = a for all a∈S.
4. For each element a∈S there exits a unique element b such that b+a = a+b = e.
The element b is known as the inverse of a, and vice versa. Obviously the identity
element e is its own inverse.
A group is called abelian if the operation + is commutative.
Examples: The set S = {…,−2,−1,0,+1,+2,…} consisting of all integer numbers
under usual addition for + forms a group. Here, 0 is the identity element and –a
is the inverse of a ∈S. This group is also abelian.
Another example is the set Zp = (0,1,2,…,p−1} of integers with modulo p addition
operation. If a = mp+q for 0 ≤ q ≤ p−a, then in modulus arithmetic a = q(modulo
p). In this group, 0 is the identity element and the integer p−a is the inverse of a,
except 0 which is its own inverse. As an example, the addition table of Z3 is shown
in Table 1.1.

10
Table1.1 Table of Z3.
+ 0 1 2
0 0 1 2
1 1 2 0
2 2 0 1
Definition 2: A set F with two operations of addition (+) and multiplication ( o ) is
a field if the following postulates hold:
1. F is abelian group under +, with the identity element denoted as e.
2. The set F − {e} is an abelian group under o , the multiplication operation.
3. The multiplication operation is distributive with respect to addition, i.e.
a o (b+c) = (a o b) + (ao c) for all a, b and c in F.
Example: Consider Zp={0,1,2, … ,p−1} again with addition (modulo p) and
multiplication (modulo p) as the two operation. It can be shown that the set
Zp−{0}={1,2, … p−1} is an abelian group if p is prime. Therefore Zp is a field if p
is a prime. The set Z2 of integers modulo 2, denoted GF(2), is an important field in
our study with:
0+0 = 0, 1+0 = 0+1 = 1, and 1+1= 0,
0 o 0 = 0, 1 o 0 = 0 o 1 = 0, and 1 o 1 = 1.
1.4.2 VECTOR SPACES
Consider a set S with a binary operation . Let F be a field with + and o being
the addition and multiplication operations, respectively. A multiplication
operation, denoted by ∗, is also defined between the elements of F and those of S.
This operation assigns to each ordered pair (α,s) a unique element denoted by α∗s,
where α is in F and s is in S. The set S is a vector space over F if the following
postulates hold:
1. S is an abelian group under .

11
2. For any elements α and β in F, and any elements s1 and s2 in S the followings
hold:
1s(∗α s2) = )s( 1∗α )s( 2∗α
and
)s(s)( 11 ∗α=∗β+α )s( 1∗β
3. For any element α and β in F and any element s in S:
)s(s)( ∗β∗α=∗β∗α
4. For any element s in S, 1∗s = s, where 1 is the multiplicative identity in F.
Consider a vector space S, over the field F. The elements of S are called vectors
and those of F are known as scalars. If an element s of S is expressible as,
s = (α1∗s1) (α2∗s2) …. (αj∗sj), (1-4)
where si’s are vectors and αi’s are scalars, then s is said to be a linear combination
of s1,s2, … ,sj. Vectors s1,s2, … ,sj are said to be linearly independent if no vectors
in this set is expressible as a linear combination of the remaining vectors in the set.
Vectors s1,s2, … ,sn form a basis in the vector space S if they are linearly
independent and every vector in S is expressible as a linear combination of these
vectors. The vectors s1,s2, … ,sn are known as basis vectors. The dimension of the
vector space S, denoted by dim(S), is the number of vectors in a basis of S. If S′
is a subset of the vector space S over F, then S′ is a subspace of S if S′ is also a
vector space on F. The direct sum S1 S2 of two subspaces S1 and S2 of S is the set
of all vectors of the form s1 s2, where s1 ∈ S1 and s2 ∈ S2. It can be proved that
S1 S2 is also a subspace, and its dimension is given by
dim(S1 S2) = dim(S1) + dim(S2) − dim(S1∩S2). (1-5)
Note that S1∩S2 is also a subspace whenever S1 and S2 are subspaces.
1.4.3 VECTOR SPACE OF A GRAPH
Consider a graph S = (N,M) and let WS denote the collection of all subsets of M,
including the empty set ∅. Under ring sum operation ⊕ between sets, in the
following it is shown that WS is an abelian group. Defining a suitable
multiplication between elements of the field Z2 and those of WS, it can be shown
that WS is a vector space over Z2.

12
It can be shown that WS is closed under ⊕. The operator ⊕ is associative and
commutative. Further for any element Mi in WS, Mi ⊕ ∅ = Mi and Mi ⊕ Mi = ∅.
Therefore for the operation ⊕, ∅ is the identity element, and each Mi is its own
inverse. Hence WS is an abelian group under ⊕.
Let ∗, a multiplication operation between the elements of Z2 and those of WS be
defined as follows:
1∗Mi = Mi and 0∗Mi = ∅.
With this definition of ∗ one can verify that the elements of WS satisfy the
following other requirements of a vector space:
1. (α+β)∗Mi = (α∗Mi) ⊕ (β∗Mi).
2. α∗(Mi ⊕ Mj) = (α∗Mi) ⊕ (α∗Mj).
3. (α.β)∗Mi = α∗(β∗Mi).
4. 1∗Mi = Mi. (Notice that 1 is the multiplicative identity in Z2.)
Therefore WS is a vector space over Z2. The dimension of this space is equal to the
number of members of the graph S.
Since each member-induced subgraph of S corresponds to a unique subset of M,
and by definition the ring sum of any two member-induced subgraphs corresponds
to the ring sum of their corresponding member sets, it is obvious that the set of all
members-induced subgraphs of S is also a vector space over Z2 if the
multiplication is defined as follows:
1∗Mi = Mi and 0∗Mi = ∅, the null graph having no nodes and no members.
This vector space will also be referred to by the symbol WS.
1.4.4 CYCLE SUBSPACE AND CUTSET SUBSPACE OF A GRAPH
Now we study two important subspaces of WS, namely cycle space and cutset
space of a graph.
Theorem 1: The set of all simple cycles and union of member-disjoint cycles of a
graph, WC, is a subspace WS of S.

13
Consider C1 and C2 as two cycles of WC. To prove the theorem one should show
that C1 ⊕ C2 is also a cycle C3 belonging to WC. Let n be a node of C3. This node
is present at least in of the two cycles C1 and C2. Let Mi (i=1,2,3), denotes the
members incident to n in Ci. Let |Ci| shows the number of members of Ci, thus |Mi|
is the number of members incident to n in Ci. Note that |M1| and |M2| are both even
and one of them may be zero. Furthermore |M3| is non-zero.
Since C3 = C1 ⊕ C2, we have:
M3 = M1 ⊕ M2.
Therefore:
|M3| = |M1| ⊕ |M2| − 2|M1∩M2|.
Since |M1| and |M2| are both even, thus |M3| is also even. This is true for all the
nodes of C3, and it follows that it is a cycle in WC and the theorem is proven.
Theorem 2: The set of all cutsets and the union of member-disjoint cutsets WC’ in
a graph S, is a subspace of the vector space WS of S.
It can be shown that the ring sum of any two cuts in a graph is a cut in S. Similarly
the union of any two member-disjoint cuts in a graph S is also a cut in S. Since
WC’ is closed under the ring sum operation thus the proof is completed.
1.4.5 FUNDAMENTAL CYCLE BASES
A special cycle basis known as a fundamental cycle basis can easily be
constructed corresponding to a tree T of S. In a connected S, a chord of T together
with T contains a cycle known as a fundamental cycle of S. Moreover, the
fundamental cycles obtained by adding the chords to T, one at a time, are
independent, because each cycle has a member which is not in the others. Also,
every cycle Ci depends on the set of fundamental cycles obtained by the above
process, for Ci is the symmetric difference of the cycles determined by the chords
of T which lie in Ci. Thus the cycle rank (cyclomatic number, first Betti number,
nullity) of graph S which is the number of cycles in a basis of the cycle space of S,
is given by,
b1(S) = M(S) − N(S) + 1, (1-6)
and if S contains b0(S) components, then:
b1(S) = M(S) − N(S) + b0(S). (1-7)

14
A formal proof is provided in Section 1.4.7.
As an example, the selected tree T and four fundamental cycles of S are illustrated
in Figure 1.12.
S T
C C1 2
C C3 4
Fig. 1.12 A graph S and a fundamental cycle basis of S.
1.4.6 FUNDAMENTAL CUTSET BASES
A basis can be constructed for the cutset space of a graph S. Consider the tree T
and its cotree T∗. The subgraph of S consisting of T∗ and any member of T
(branch) contains exactly one cutset known as a fundamental cutset. The set of
cutsets obtained by adding branches of T to T∗, one at a time, forms a basis for the
cutset space of S, known as a fundamental cutset basis of S. The cutset rank (rank
of S) is the number of cutsets in a basis for the cutset space of S, which is given by
ρ(S) = N(S) − 1, (1-8)
and for a graph with b0(S) components:
ρ(S) = N(S) − b0(S). (1-9)
A formal proof is provided in Section 1.4.7.
A graph S and a fundamental cutset basis of S are shown in Figure 1.13. A branch
of the tree subdivides the nodes of the tree into two subsets. The members of a
cutest should have one end in each subsets.

15
(a) A graph S. (b) A tree T of S. (c) Cotree T* of T.
C
*
*
C
*
C
*
C
*
C
*
C
1 2
3
4 5 6
Fig. 1.13 A graph S and a fundamental cutset basis of S.
1.4.7 DIMENSION OF CYCLE AND CUTSET SUBSPACES
Consider T as a spanning tree of a connected graph S with N nodes and M
members. The branches of T are denoted by b1,b2, …, bN-1 and the chords by c1,c2,
… ,cM−N+1. Let Ci and iC′ refer to the fundamental cycle and the fundamental
cutset with respect to ci and bi , respectively.
Since each fundamental cycle contains exactly one chord, and this chord is present
in no other fundamental cycle, therefore the fundamental cycles C1, C2, … ,CM−N+1
are independent. Using a similar reasoning for cutsets, it becomes obvious that all
the fundamental cutsets 1C′ , 2C′ , … , 1NC −′ are also independent.
Now we need to prove that every subgraph in cycle (cutset) subspace of S can be
expressed as a ring sum of Ci ( iC′ ). For this purpose consider any subgraph C in
the cycle space of S. Let C contain the chords ci1,ci2,…,cir. Let C′ denote the ring
sum of the fundamental cycle Ci1,Ci2,…,Cir. Obviously the chords ci1,ci2,…,cir are
present in C′ , and C′ contains no other chord of the T. Since C also contains these
chords and no others, C′ ⊕ C contains no chords.

16
Now it is claimed that C′ ⊕ C is empty. If this is not true, then by the preceding
arguments, C′ ⊕ C contains only branches and has no cycle. On the other hand,
being a ring sum of cycles, C′ ⊕ C is a cycle of the union of member-disjoint
cycles. Therefore the assumption that C′ ⊕C is not empty leads to a contradiction.
Hence C′ ⊕ C is empty. This implies that C = C′ = Ci1⊕ Ci2 ⊕ … ⊕ Cir , i.e. every
subgraph in the cycle space of S can be expressed as a ring sum of the
fundamental cycles.
In a similar manner it can be proved that every subgraph in the cutset subspace of
S can be expressed as a ring sum of the fundamental cutsets.
The following fact can now be concluded:
1. The fundamental cycles with respect to a spanning tree of S constitute a basis
for the cycle subspace of S, and therefore the dimension of the cycle subspace of S
is equal to M−N+1.
2. The fundamental cutsets with respect to a spanning tree of S constitute a basis
for the cutset subspace of S, and therefore the dimension of the cutset subspace of
S is equal to N−1.
For graphs which are not connected, spanning forest will replace the spanning tree,
and the dimensions for cycle subspace and cutset subspace will then be nullity of S
= b1(S) = M−N+b0(S) and rank of S = ρ(S) = N−b0(S), respectively. Here, b0(S) is
the number of components of S.
1.4.8 ORTHOGONALITY PROPERTY
Two vectors are called orthogonal if their scalar product is zero. It can be shown
that a vector is a cycle set (cutset) vector, if and only if it is orthogonal to every
vector of a cutset (cycle set) basis. Since the cycle set and cutset spaces of a graph
S containing M(S) members are both subspaces of the M(S)-dimensional space of
all vectors which represent subsets of the members, therefore the cycle set and
cutset spaces are orthogonal components of each other.
1.5 MATRICES ASSOCIATED WITH A GRAPH
Matrices play a dominant role in the theory of graphs and in particular in its
applications to structural analysis. Some of these matrices conveniently describe
the connectivity properties of a graph and others provide useful information about
the patterns of the structural matrices, and some reveal additional information
about transformations such as those of equilibrium and compatibility equations.

17
In this section various matrices are studied which reflect the properties of the
corresponding graphs. For simplicity, all the graphs are assumed to be connected,
since the generalization to non-connected graphs is trivial and consists of
considering the direct sum of the matrices for their components.
1.5.1 MATRIX REPRESENTATION OF A GRAPH
A graph can be represented in various forms. Some of these representations are of
theoretical importance, others are useful from the programming point of view
when applied to realistic problems. In this section six different representations of a
graph are described.
Node Adjacency Matrix: Let S be a graph with N nodes. The adjacency matrix A
is an N×N matrix in which the entry in row i and column j is 1 if node ni is
adjacent to nj , and is 0 otherwise. This matrix is symmetric and the row sums of A
are the degrees of the nodes of S.
The adjacency matrix of the graph S, shown in Figure 1.14, is a 5×5 matrix as:
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎡
=
01100
10110
11011
01101
00110
n
n
n
n
n
nnnnn
5
4
3
2
1
54321
A
(1-10)
n n
nnn m
m
m
m m m
1
2
3
4
51
3 4 5 6
7
2m
Fig. 1.14 A graph S.
It can be noted that A is a symmetric matrix of trace zero. For two isomorphic
graphs S and S', the adjacency matrix A of S can be transformed to A' of S' by
simultaneous permutations of the rows of A. The (i,j)th entry of A2
shows the
number of walks of length 2 with ni and nj as end nodes. Similarly, the entry in the

18
(i,j) position of Ak
is equal to the number of walks of length k with ni and nj as
end nodes. The polynomial,
)det()( AI −λ=λφ , (1-11)
is called the characteristic polynomial of S. The collection of N(S) eigenvalues of
A is known as the spectrum of S. Since A is symmetric, the spectrum of S consists
of N(S) real numbers. The sum of eigenvalues of A is equal to zero. The
eigenvectors of A are orthogonal.
Node-Member Incidence Matrix: Let S be a graph with M members and N
nodes. The node-member incidence matrix B is an N×M matrix in which the
entry in row i and column j is 1 if node ni is incident with member mj, and is 0
otherwise. As an example, the node-member incidence matrix of the graph in
Figure 1.14 is a 5×7 matrix of the form:
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎡
=
0100010
1110000
0011011
1001100
0000101
5
4
3
2
1
7654321
n
n
n
n
n
mmmmmmm
B
(1-12)
Obviously, the pattern of an incidence matrix depends on the particular way that
its nodes and members are labelled. One incidence matrix can be obtained from
another by simply interchanging rows (corresponding to relabelling the nodes) and
columns (corresponding to relabelling the members).
The incidence matrix B and the adjacency matrix A of a graph S are related by,
,t
VABB += (1-13)
where V is a diagonal matrix of order N(S), known as the degree matrix, whose
typical non-zero entry vii is the valency of the node ni of S for i=1, ... ,N(S).
The rows of B are dependent and one row can arbitrarily be deleted to ensure the
independence of the rest of the rows. The node corresponding to the deleted row is
called a datum (reference) node. The matrix obtained after deleting a dependent
row is called an incidence matrix of S and it is denoted by B.
Although A and B are of great theoretical value, however, the storage
requirements for these matrices are high and proportional to N×N and M×(N−1),

19
respectively. In fact a large number of unnecessary zeros is stored in these
matrices. In practice one can use different approaches to reduce the storage
required, some of which are described in the following.
Member List: This type of representation is a common approach in structural
mechanics. A member list consists of two rows (or columns) and M columns (or
rows). Each column (or row) contains the labels of the two end nodes of each
member, in which members are arranged sequentially. For example, the member
list of S in Figure 1.14 is:
.
4543253
2432131
n
n
j
i
7m6m5m4m3m2m1m
⎥
⎦
⎤
⎢
⎣
⎡
=ML (1-14)
It should be noted that a member list can also represent orientations on members.
The storage required for this representation is 2×M. Some engineers prefer to add
a third row containing the member's labels, for easy addressing. In this case the
storage is increased to 3×M.
A different way of preparing a member list is to use a vector containing the end
nodes of members sequentially; e.g. for the previous example this vector becomes:
(1,3 ; 3,5 ; 1,2 ; 2,3 ; 3,4 ; 4,5 ; 2,4 ). (1-15)
This is a compact description of a graph; however, it is impractical because of the
extra search required for its use in various algorithms.
Adjacency List: This list consists of N rows and D columns, where D is the
maximum degree of the nodes of S. The ith row contains the labels of the nodes
adjacent to node i of S. For the graph S shown in Figure 1.14, the adjacency list is:
DN5
4
3
2
1
43
532
5421
431
32
n
n
n
n
n
×
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎣
⎡
=AL (1-16)
The storage needed for an adjacency list is N×D.
Compact Adjacency List: In this list the rows of AL are continually arranged in
a row vector R, and an additional vector of pointers P is considered. For example,
the compact adjacency list of Figure 1.14 can be written as:

20
R = (2,3,1,3,4,6,2,4,5,2,3,5,3,4 ),
P = (1,3,6,10,13,15). (1-17)
P is a vector (p1, p2, p3, … ) which helps to list the nodes adjacent to each node.
For node ni one should start reading R at entry pi and finish at pi+1 − 1.
An additional restriction can be put on R, by ordering the nodes adjacent to each
node ni in ascending order of their degrees. This ordering can be of some
advantage, an example of which is nodal ordering for bandwidth optimisation. The
storage required for this list is 2M + N + 1.
1.5.2 CYCLE BASES MATRICES
The cycle-member incidence matrix C of a graph S, has a row for each cycle or
hinged cycle and a column for each member. An entry ijc of C is 1 if cycle Ci
contains member mj and it is 0 otherwise. In contrast to the node adjacency and
node-member incidence matrix, the cycle-member incidence matrix does not
determine a graph up to isomorphism; i.e. two totally different graphs may have
the same cycle-member incidence matrix.
For a graph S there exists 12 )S(b1 − cycles or hinged cycles. Thus C is a
( 12 )S(b1 − )×M matrix. However, one does not need all the cycles of S, and the
elements of a cycle basis are sufficient. For a cycle basis, a cycle-member
incidence matrix becomes a b1(S)×M matrix, denoted by C, known as the cycle
basis incidence matrix of S. As an example, matrix C for the graph shown in
Figure 1.14, for the following cycle basis,
C1 = (m1 , m3 , m4 )
C2 = (m2 , m5 , m6 )
C3 = (m4 , m5 , m7 )
is given by:
.
1011000
0110010
0001101
C
C
C
mmmmmmm
3
2
1
7654321
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎣
⎡
=C
(1-18)

21
The cycle adjacency matrix D = CCt
– W is a b1(S)×b1(S) matrix, each entry dij of
which is 1 if Ci and Cj have at least one member in common, and it is 0 otherwise.
For the above example,
,
011
100
100
300
030
003
311
130
103
t
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎣
⎡
=
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎣
⎡
−
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎣
⎡
=−= WCCD (1-19)
where W is a diagonal matrix, in which a typical non-zero entry wii is the length of
the cycle Ci. The trace of CCt
is equal to the total length of the cycles in the basis.
An important theorem can now be proved which is based on the orthogonality
property mentioned in Section 1.4.8.
Theorem: Let S have incidence matrix B and a cycle basis incidence matrix C.
Then:
CBt
= 0 (mod 2). (1-20)
Proof: Consider the ith row of C and the jth column of Bt
, which is the jth row of
B. The rth entry in these two rows are both non-zero if and only if mr is in cycle Ci
and is incident with nj. If mr is in Ci, then nj is also in Ci, but if nj is in the cycle,
then there are two members of Ci incident with nj so that the (i,j)th entry of CBt
is
1+1 = 0(mod 2), and this completes the proof.
Matrix C for a fundamental cycle basis with special labels for its tree members
and chords, finds a particular pattern. Let S have a tree T whose members are
M(T) = (m1, m2, ... ,mp) and a cotree for which M(T*) = (mp+1, mp+2, …mp+M(S)).
Then there is a unique fundamental cycle Ci in S − M(T*) + mi , p+1 ≤ i ≤ M(S)
and this set of cycles forms a basis for the cycle space of S. As an example, for the
graph S of Figure 1.12 (page 14) whose members are labelled as shown in Figure
1.15, the fundamental cycle basis consists of:
C1 = (m1, m2, m7 ) C2 = (m2, m3, m8 )
C3= (m1, m4, m9, m5, m2 ), C4 = (m2, m5, m10, m6, m3 ).
The corresponding C for the selected tree T is denoted by C0 and has the
following form:

22
*)T(M)T(M
mmmmmmmmmm
4
3
2
1
0
10987654321
][
1000110110
0100011011
0010000110
0001000011
C
C
C
C
C T IC==
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎡
(1-21)
n
n
n
n
n
n
n
5
6
4
7
1
2
3
9 10
7 8
1 3
4 5 6
2
Fig. 1.15 A graph and its tree members.
1.5.3 CUTSET BASES MATRICES
The cutset-member incidence matrix *
C for a graph S, has a row for each cutset of
S and a column for each member. An entry *
ijc of *
C is 1 if cutset *
iC contains
member mj and it is 0 otherwise. This matrix like C does not determine a graph
completely. Independent rows of *
C for a cutset basis, denoted by C*, form a
matrix known as a cutset basis incidence matrix, which is a ρ(S)×M matrix, ρ(S)
being the rank of graph S. As an example, C* for the cutset of Figure 1.13 with
members labelled as in Figure 1.15, is given below:
.
1000100000
1010000100
1100010000
1111000010
0100001000
0101000001
*
*
6C
*
5C
*
4C
*
3C
*
2C
*
1C
10
m
9
m
8
m
7
m
6
m
5
m
4
m
3
m
2
m
1
m
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎡
=C
(1-22)

23
The cutset adjacency matrix t∗∗∗
= CCD is a ρ(S)×ρ(S) matrix defined
analogously to cycle adjacency matrix D.
For a fundamental cutset basis with appropriate labelling of the members in T and
T*, the particular pattern of C* becomes:
[ ]*
c
*
0
1000100000
1100010000
0100001000
1010000100
1111000010
0101000001
CIC =
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎡
= (1-23)
From the orthogonality condition, 0CC =t*
00 , hence:
[ ] .t*
c
T 0
C
I
IC =⎥⎦
⎤
⎢⎣
⎡
(1-24)
:inresulting,)2(modHence t
cT 0CC =+ ∗
t*
cT CC = . (1-25)
Therefore, for a graph having C0, one can construct *
0C and vice versa.
There exists a very simple basis for the cutset space of a graph, which consists of
N−1 cocycles of S. As an example, for the graph of Figure 1.14, considering n5 as
a datum node, we have,
,
1110000
0011011
1001100
0000101
*
7654321 mmmmmmm
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎡
=C
(1-26)
which is the same as the incidence matrix B of S. The simplicity of the
displacement method of structural analysis is due to the existence of such a simple
basis.

24
1.6 DIRECTED GRAPHS AND THEIR MATRICES
An oriented or directed graph is a graph in which each member is assigned an
orientation. A member is oriented from its initial node to its final node, as shown
in Figure 1.16(a). The initial node is said to be positively incident on the member
and the final node negatively incident, as shown in the Figure:
n
n
n
n
n
n
n
5
6
4
7
1
2
3
9 10
7 8
1 3
4 5 6n
n
j
i
Ci
*
(a) (b) (c)
Fig. 1.16 An oriented member, a directed graph and a directed tree.
The choice of orientation of members of a graph is arbitrary; however, once it is
chosen, it must be retained. Cycles and cutsets can also be oriented as shown in
Figure 1.16(b).
As an example, m7 is positively oriented in cycle Ci, and m10 is negatively oriented
in cutset *
iC .
All the matrices B , B, C and C* can be defined as before, with the difference of
having +1, −1 and 0 as entries, according to whether the member is positively,
negatively and zero incident with a cutset or a cycle.
As an example, for graph S in Figure 1.16(b) the matrix B with n1 as datum node
is formed:
.
1000100000
1100010000
0100001000
0010100100
0011010010
0001001001
n
n
n
n
n
n
7
6
5
4
3
2
10m9m8m7m6m5m4m3m2m1m
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎡
−
−
−
−−
−−
−
=B (1-27)

25
Consider a tree as shown in Figure 1.16(c). The corresponding cycle basis
incidence matrix can be written as:
cT
4
3
2
1
C
C
C
C
.
1000110110
0100011011
0010000110
0001000011
10m9m8m7m6m5m4m3m2m1m
CC
C
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎡
−−
−
=
(1-28)
Obviously
,
BCt
= CBt
= 0(mod 2), (1-29)
with a similar proof as that of the non-oriented case.
A cutset-member incidence matrix is similarly obtained as:
∗∗
∗
∗
∗
∗
∗
∗
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎡
−
−
−
−−
−−
−−
=
cT
6
5
4
3
2
1
.
1000100000
1100010000
0100001000
1010000100
1111000010
0101000001
C
C
C
C
C
C
*
10
m
9
m
8
m
7
m
6
m
5
m
4
m
3
m
2
m
1
m
CC
C
(1-30)
It can easily be proved that:
t*
cT CC −= . (1-31)
1.7 GRAPHS ASSOCIATED WITH MATRICES
Matrices associated with graphs are discussed in the previous sections. Sometimes
it is useful to consider the reverse of this process and think of the graph associated
with an arbitrary matrix H. Such a graph has a node associated with each row of
the matrix and if hij is non-zero, then there is a connecting member from node i to
node j. In the case of a symmetric matrix, there is always a connection from i to j
whenever there is one from j to I ; therefore one can simply use undirected

26
members. Two simple examples are illustrated in Figure 1.17 and Figure 1.18. The
directed graph associated with a non-symmetric matrix is usually called a digraph
and the word graph is used for the undirected graph associated with a symmetric
matrix:
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎣
⎡
=
*0**
***0
0**0
00**
H
Fig. 1.17 A non-symmetric matrix H and its associated digraph.
⎥
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎢
⎣
⎡
=
****
***0
****
*0**
H
1
2
3
4
Fig. 1.18 A symmetric matrix H and its associated graph.
For H to be viewed as the adjacency matrix, due to the presence of diagonal
entries, one loop should be added to each node. However, since the structural
models have always non-zero diagonal entries and contain no loops, this addition
is disregarded.
With a rectangular matrix E a bipartite graph S = (A,B) can be associated. For
each row of E a node of A and with each column of E a node of B is associated.
Two nodes of A and B are connected with a member of S if eij is non-zero. An
example of this is shown in Figure 1.19.
⎥
⎥
⎥
⎦
⎤
⎢
⎢
⎢
⎣
⎡
=
0**0
**0*
00**
E BA

27
Fig. 1.19 A rectangular matrix E and its associated bipartite graph.
A weighted bipartite graph can be defined for any m×n rectangular matrix H. The
nodes r1,r2,…,rm and nodes c1,c2,…,cn of the graph correspond to rows and
columns of H, respectively. If hij ≠ 0, then ri is joined to cj by a member whose
weight is hij.
1.8 PLANAR GRAPHS - EULER´S POLYHEDRA FORMULA
Graph theory and properties of planar graphs, were first discovered by Euler in
1736. After 190 years Kuratowski found a criterion for a graph to be planar.
Whitney developed some important properties of embedding graphs in the plane.
MacLane expressed the planarity in terms of the graph´s cycle basis. In this
section some of these criteria are studied, and Euler´s polyhedra formula is proven.
1.8.1 PLANAR GRAPHS
A graph S is called planar if it can be drawn (embedded) in the plane in such a
way that no two members cross each other. As an example, a complete graph K4
shown in Figure 1.20 is planar since it can be drawn in the plane as shown:
(a) K4. (b) Planar drawings of K4.
Fig. 1.20 K4 and two of its drawings.
On the other hand K5, Figure 1.21, is not planar, since every drawing of K5
contains at least one crossing.
(a) K5. (b) Two drawings of K5 with one crossing.

28
Fig. 1.21 K5 and two of its drawings.
Similarly K3,3, Figure 1.22, is not planar, as illustrated:
(a) K3,3. (b) Two drawings of K3,3 with one crossing.
Fig. 1.22 K3,3 and its drawings.
A planar graph S drawn in the plane divides the plane into regions all of which are
bounded and only one is unbounded. If S is drawn on a sphere, all the regions will
be bounded; however, the number of regions will not change. The cycle bounding
a region is called a regional cycle. Obviously the sum of the lengths of regional
cycles is twice the number of members of the graph.
There is an outstanding formula that relates the number of regions, members and
nodes of a planar graph, in the form,
R(S) − M(S) + N(S) = 2,
where R(S), M(S) and N(S) are the numbers of regions, members and nodes of
planar graph S, respectively. This formula shows that for different drawings of S
in the plane, R(S) remains constant.
Originally the above relationship was given for polyhedra, in which R(S), M(S)
and N(S) correspond to faces, edges and corners of a polyhedron, respectively.
However, the theorem can easily be expressed in graph-theoretical terms as
follows.
Theorem (Euler [45]): Let S be a connected planar graph. Then:
R(S) − M(S) + N(S) = 2. (1-32)
Proof: For proof, S is reformed in two stages. In the first stage, a spanning tree T
of S is considered in the plane for which R(T) − M(T) + N(T) = 2. This is true
since R(T) = 1 and M(T) = N(T) − 1. In second stage chords are added one at a
time. Addition of a chord increases the number of members and regions each by

29
unity, leaving the left hand side of Eq. (1-32) unchanged during the entire process,
and the result follows.
1.8.2 THEOREMS FOR PLANARITY
In order to check the planarity of a graph, different approaches are available which
are based on the following theorems. These theorems are only stated and the
reader may refer to textbooks on graph theory for proofs.
Theorem (Kuratowski [148]): A graph S is planar if and only if it has no subgraph
contractible to K5 or K3,3 .
Contracting a member mk = (ni,nj) is an operation in which the member is removed
and ni is identified with nj so that the resulting node is incident to all members
(other than mk) that were originally incident with ni or nj. If a graph S′ can be
obtained from S by succession of member contractions, then S is contractible to
S′. The process of the contraction of a member (ni,nj) of a graph is shown in
Figure 1.23(a), and the contraction of the Petersen graph to K5 is illustrated in
Figure 1.23(b).
nn i j in jn n jn i,
mk
(a) Contraction of a member mk.
(b) Contraction of the Petersen graph to K5.
Fig. 1.23 The contraction of a member in a graph.

30
Theorem (MacLane [167]): A connected graph is planar if and only if every block
of S with at least three nodes has a cycle basis, )S(1b21 C,...,C,C and one additional
cycle C0, such that every member is contained in exactly two of these b1(S)+1
cycles.
A block is a maximal non-separable graph, and a non-separable graph is a graph
that has no cut-points. A cut-point is a node whose removal increases the number
of components and a bridge is such a member. In Figure 1.24, a graph and its
blocks are illustrated:
bridge cut-point
Fig. 1.24 A graph and its blocks.
Definitions: A graph S* is a dual graph of a graph S if there is a 1-1
correspondence between the members of S* and those of S, such that a set of
members in S* is a cycle vector of S* if and only if the corresponding set of
members in S is a cutset vector of S.
Theorem (Whitney [250]) - A graph is planar if and only if it has a combinatorial
dual.
(a) A planar graph S. (b) The dual graph of S.
Fig. 1.25 A planar graph and its dual.

31
For a connected planar graph S, the dual graph S* is constructed as follows:
To each region ri of S there is a corresponding node ∗
ir of S* and to each member
mj of S there is a corresponding member ∗
jm in S*, such that if the member mj
occurs on the boundary of two regions r1 and r2, then the member ∗
jm joins the
corresponding nodes ∗
1r and ∗
2r in S*, Figure 1.25.
1.9 MAXIMAL MATCHING IN BIPARTITE GRAPHS
1.9.1 DEFINITIONS
As defined before, a graph is bipartite if its set of nodes can be partitioned into two
sets A and B, such that every member of the graph has one end node in A and
other in B. Such a graph is denoted by S = (A,B). A set of members of S is called a
matching if no two members have a common node. The size of any largest
matching in S is called the matching number of S, denoted by ψ(S). A subset N'(S)
⊆ N(S), is the node cover of S, if each member of S has at least one end node in
N'(S). The cardinality of any smallest node cover, denoted by τ(S), is known as the
node covering number of S.
1.9.2 THEOREMS ON MATCHING
In this section, three theorems are stated, and the proofs may be found in the book
by Lovasz and Plumner [164]:
Theorem 1 (König [141,142]): For a bipartite graph S, the matching number ψ(S)
is equal to the node covering number τ(S).
Theorem 2 (Hall [69]): Let S = (A,B) be a bipartite graph. Then S has a complete
matching of A into B if and only if |Γ(X) | ≥ |X| for all X ⊆ A.
Γ(X) is the image of X, i.e. those elements of B which are connected to the
elements of X in S. Figure 1.26(a) shows a bipartite graph for which matching
exists and Figure 1.26(b) illustrates a case where complete matching does not
exist, because X = (a1, a2) are matched to b1, i.e. |Γ(X)| ≤ |X|:

32
A B A B
(a) (b)
a
a
b1
2
1
Fig. 1.26 Matching in bipartite graphs.
A perfect matching is a matching which covers all nodes of S.
Theorem 3 (Frobenius [53]): A bipartite graph S = (A,B) has a perfect matching if
and only if |A| = |B| and for each X ⊆ A, |Γ(X) | ≥ |X|.
(a) Perfect matching exists. (b) Perfect matching does not exist.
Fig. 1.27 Perfect matching in bipartite graphs.
This is also known as the marriage theorem. Figures 1.27(a) and (b) show cases
when a perfect matching exists and does not exist, respectively.
Therefore Frobenius´s theorem characterizes those bipartite graphs which have a
perfect matching. Hall´s theorem characterizes those bipartite graphs that have a
matching of A into B. König´s theorem gives a formula for the matching number
of a bipartite graph.
1.9.3 MAXIMUM MATCHING

33
Let M be any matching in a bipartite graph S = (A,B). A path P is called an
alternating path with respect to M, or an M-alternating path if its members
(edges) are alternately chosen from the matching M and outside M. A node is
exposed (unmatched, not covered) with respect to matching M if no member of M
is incident with that node. An alternating tree relative to the matching, is a tree
which satisfies the following two conditions: first, the tree contains exactly one
exposed node from A, which is called its root, second, all paths between the root
and any other node in the tree are alternating paths.
As an example, in Figure 1.28(a) the path a1b1a3b3a4 is an alternating path with
respect to the matching shown in bold lines; a2, b2, a4 and b4 are exposed nodes.
b
b
b
b
a
a
a
a
1
2
3
4 4
3
2
1
b
b
b
b
4
3
2
1 1
2
3
4a
a
a
a
(a) An arbitrary matching. (b) An augmented matching.
Fig. 1.28 Operation for maximum matching.
An M-alternating path joining two exposed nodes is called an M-augmenting path.
For every such path the corresponding matching can be made larger by discarding
the members of P ∩ M and adding those of P − M, where P is an M-alternating
path b2a1b1a3b3a4, Figure 1.28(b). Thus, if S contains any M-alternating path P
joining two exposed nodes, then M can not be a maximum matching, for one can
readily obtain a larger matching M´ by discarding the members of P ∩ M and
adding those of P − M.
Theorem 4 (Berge [13-14]): Let M be a matching in a graph S. Then M is a
maximum matching if and only if there exists no augmenting path in S relative to
M.
The above result provides a method for finding a maximum matching in S. The
computational procedure for construction of a maximum matching begins with
considering any feasible matching, possibly the empty matching. Each exposed
node of A is made the root of an alternating tree, and nodes and members are
added to the trees by means of a labelling technique. Eventually, the following two
cases must occur: either an exposed node in B is added to one of the trees, or else

34
it is not possible to add more nodes and members to any of the trees. In the former
case, the matching is augmented and the formation of trees is repeated with respect
to the new matching. In the latter case, the trees are said to be Hungarian and the
process is terminated.
As an example, consider the matching shown in Figure 1.29(a), in which bold
lines represent members in the matching. Alternating trees are constructed, with
the exposed nodes a1 and a5 of A as roots, as shown in Figure 1.29(b). An
augmenting path is found, as indicated in the Figure. Naturally, several different
sets of alternating trees could have been constructed. For example, the tree rooted
at node a1 could have contained the member (a2,b3).
a
a
a
a b
b
b
a
b
b
1
2
3
4
5 5
4
3
2
1
b
b
b
b
a a
a
a
1
2
3
4
4
3
21
a
b5
5
b5
(a) A bipartite graph S. (b) Alternating trees.
Fig. 1.29 A bipartite graph and its alternating tree.
Augmented matching is shown in Figure 1.30. When the alternating tree of Figure
1.31 is used for the augmented matching, it becomes Hungarian.
a
a
a
a b
b
b
a
b
b
1
2
3
4
5 5
4
3
2
1
Fig. 1.30 Augmented matching.
a b a b a b1 2 2 3 5 5 4
a
Fig. 1.31 Alternating tree for augmented matching.

35
BIPARTITE MATCHING ALGORITHM
Let X be any matching, possibly the empty matching of a bipartite graph S =
(A,B). No nodes are labelled.
Step 1 (labelling):
1.1 Give the label ∅ to each exposed node in A.
1.2 If there are no unscanned labels, go to Step 3. Otherwise, find a node i with
an unscanned label. If i ∈ A, go to Step 1.3; if i ∈ B, go to Step 1.4.
1.3 Scan the label on node i (i∈A) as follows: for each member (i,j) ∉ X
incident to node i, give node j the label "i", unless node j is already
labelled. Return to Step 1.2.
1.4 Scan the label on node i (i∈B) as follows: if node i is exposed, go to Step 2.
Otherwise, identify the unique member (i,j)∈X incident to node i and give
node j the label "i". Return to Step 1.2.
Step 2 (Augmenting):
An augmenting path has been found, terminating at node i (identified in Step 1.4).
The nodes preceding node i in the path, are identified by backtracking. That is, if
the label on node i is "j", the second-to-last node in the path is j. If the label on
node j is "k", the third-to-last node is k, and so on. The initial node in the path has
the label "∅". Augment X by adding to X all members in the augmenting path that
are not in X and removing those which are in X. Remove all labels from nodes.
Return to Step 1.1.
Step 3 (Hungarian Labelling):
The labelling is Hungarian, no augmenting path exists, and the matching X is of
maximum cardinality.
For further study, the reader may refer to the original paper of Hopcroft and Karp
[80] or Lawler [157]. An algorithm using a different approach may be found in
Ref. [6].

36
EXERCISES
1.1 In the following graph, which members are incident with node 3 (identify
with their end nodes)? Which nodes are adjacent to node 4? What is the degree of
node 2?
5
6
1
4
2
3
1.2 Are the following graphs isomorphic?
1
2
3
4
5
6
1 2
65
3 4
1.3 Draw a tree, a spanning tree and an SRT rooted at O for the following
graph. Use O′ as the root of a second SRT and compare its length and width with
those of the first one.
O O'
1.4 What types of graph are the following?

37
1.5 List all the cycles of the following graph:
1.6 Prove that for a planar graph embedded on a sphere with all triangular faces,
M(S) = 3N(S) − 6.
1.7 Find a fundamental cycle basis of the following graph using an arbitrary
spanning tree:
O
O'
1.8 In the above example use two SRTs rooted at O and O′ and compare the
length of the corresponding fundamental cycle bases.
1.9 Write the adjacency and member-node incidence matrices of the graphs in
Exercise 1.2. Use an arbitrary node and member numbering. What can you say
about the resulting matrices?
1.10 Write C, C*, C0 and ∗
0C matrices for the following graph and examine the
orthogonality property:

38
1.11 Identify the planar graphs in the following figure:
(a) (b) (c) (d)
1.12 Prove that K5 and K3,3 are not planar.
1.13 Euler’s formula as in Eq. (1-32) fails for disconnected graphs. If a planar
graph S has b0(S) components, how can the formula be adjusted?
1.14 Find a maximal matching for the following bipartite graphs. Which one is
complete and which one is a perfect matching?
(a) (b)
1.15 Why is there no complete matching for the following bipartite graphs?

39
(a) (b)

Graph theory

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