Graph Theory

Introduction to Graph Theory
• What is Graph Theory?
• Study of graphs which are mathematical structures
• Graph consists of vertices and edges which connects the vertices
• Where we are using Graph Theory?

• Where in computer science, we are using Graph Theory?

• Graphs can be used to
• Determine whether it is possible to walk down all the streets in a city without going
down a street twice, and we can find the number of colors needed to color the regions
of a map.
• Design a circuit
• Determine a shortest route to travel

Graph Theory
• What is Graph?
• A graph is G=(V,E) consists of V, a non empty set of vertices (or nodes) and E, a set of
edges.
• Each edge is either one or two vertices associated with it, called as endpoints.
• An edge is said to be connected to the endpoints.
• A graph with infinite vertex set is called as an infinite graph
• A graph with a finite vertex set is called as a finite graph.
• Note that
• Each edge of the graph representing this computer network connects
two different vertices. That is, no edge connects a vertex to itself.
• No two different edges connect the same pair of vertices.

Types of Graphs
• Simple Graph
• A graph in which each edge connects two different vertices and where no two edges
connect the same pair of vertices.
• Each edge is associated to an unordered pair of vertices, and no other edge is
associated to this same edge.
• Edge connecting to vertex a and vertex b (i.e. {a,b}) no other edge is associated to edge
{a,b}
• Multigraph
• A graph in which multiple edges connects to the same pair of vertices.
• When there are m different edges associated to the same unordered pair of
vertices {a,b}, we say that {a,b} is an edge of multiplicity m.
• This set of edges connecting to a and b are m different copies of an edge {a,b}
• Pseudograph
• Loops: the edges that connect a vertex itself
• A graph containing loops and multiple edges connecting the same pair of
vertices are sometimes called as pseudograph.
a
b
c
a
b
c
Simple graph
Multigraph
Pseudograph

Types of Graphs
• Undirected Graphs
• A graph containing undirected edges.
• Directed Graphs(Diagraph)
• A graph (V,E) consists of a non-empty set of vertices V and a set of directed
edges (or arcs) E.
• Each directed edge is associated with an ordered pair of vertices.
• The directed edge associated with the ordered pair (a,b) is said to start at a
and end at b.
• An arrow pointing from a to b indicate the direction of an edge that starts at
a and ends at b.
• Can have loops and may also contain multiple directed edges starting and
ending at same vertices.
• May contain directed edges that connect vertices a and b in both directions

Types of Graphs
• Directed Graphs(Diagraph)
• A directed graph with no loops and has no multiple directed edges from a
and b is called as simple directed graph.
• Has at most one edge associated to each ordered pair of vertices
• Directed graphs that may have multiple directed edges from a vertex to a
second (possibly the same) vertex is called as directed multigraphs.
• When there are m directed edges, each associated to an ordered pair of vertices
(a, b), we say that (a, b) is an edge of multiplicity m.
• Mixed Graph
• A graph with both directed and undirected edges is called a mixed graph.
• Used to model a computer network containing links that operate in both
directions and other links that operate only in one direction.
a b
c

Types of Graphs
Type Edges Multiple edges allowed? Loops allowed?
Simple graph Undirected No No
Multigraph Undirected Yes No
Pseudograph Undirected Yes Yes
Simple directed graph Directed No No
Directed multigraph Directed Yes Yes
Mixed graph Directed or undirected Yes Yes
Table: Graph Terminology

Types of Graphs
A B
C
D
2
1
5
3 4
a
c
d
b
e
f
Simple directed graph
Directed multigraph
Multigraph

Graph Terminologies of Undirected Graph
• Two vertices u and v in an undirected graph G are called adjacent (or neighbours) in G if u
and v are endpoints of an edge of G.
• If edge e is associated with {u,v}, the edge e is called incident with the vertices u and v.
• The edge e is said to connect u and v
• The vertices u and v are called as endpoints of an edge associated with {u,v }.
• The degree of vertex in an undirected graph is the number of edges incident with it, except
that a loop at a vertex contributes twice to the degree of that vertex.
• The degree of vertex is denoted by deg(v).
a
c
d
b
e
f
What is the degree of vertex
1. a
2. b
3. c
4. d
5. e
6. f
What is the degree of vertex
1. a  4
2. b  4
3. c  2
4. d  2
5. e  3
6. f  2

• A vertex of degree zero is called isolated.
• Such isolated vertex is not adjacent to any vertex.
• A vertex is a pendant if and only if it has degree one.
• A pendant vertex is adjacent to one and only one other vertex.

• The Handshaking Theorem
Let G = (V, E) be an undirected graph with e edges. Then
2𝑒 =
𝑣∈𝑉
deg(𝑣)
This applies even if multiple edges and loops are present in the graph.
Q. How many edges are there in a graph with 12 vertices each of degree six?
• An undirected graph has an even number of edges

• An undirected graph has an even number of vertices has odd degree.
Proof
Let 𝑉1 and 𝑉2 be the set of vertices of even degree and set of vertices of odd degree
respectively, in an undirected graph. Then
2𝑒 =
𝑣∈𝑉
deg(𝑣) =
𝑣∈𝑉1
deg(𝑣) +
𝑣∈𝑉2
deg(𝑣)
C E
D
B
G
A
F
H

Graph Terminologies of Directed Graph
• When (u,v) is an edge of the graph G with directed edges,
• u is said to be adjacent to v and v is said to be adjacent from u.
• The vertex u is called as initial vertex of (u,v), and v is called as terminal
or end vertex of (u,v).
• The initial vertex and terminal vertex of a loop are the same.
• In a graph with directed edges
• The in-degree of a vertex v, denoted by deg 𝑣 is the number of edges
with v as their terminal vertex.
• The out-degree of a vertex v, denoted by deg+(v) is the number of edges
with v as their initial vertex.
• A loop at a vertex contributes 1 to both the in-degree and the out-degree
of this vertex.
2
1
5
3 4

Graph Terminologies of Directed Graph
Let G=(V,E) be a graph with directed edges. Then
𝑣∈𝑉
𝑑𝑒𝑔 (𝑣) =
𝑣∈𝑉
𝑑𝑒𝑔
+
𝑣 = 𝐸

Special Graph Terminologies
• Complete Graph
• The complete graph on n vertices, denoted by Kn, is the simple graph that contains
exactly one edge between each pair of distinct vertices.
• Cycles
• The cycle Cn, 𝑛 ≥ 3, consists of n vertices 𝑣1, 𝑣2, . . . , 𝑣𝑛 and edges {𝑣1, 𝑣2}, {𝑣2, 𝑣3},- ..
,{𝑣𝑛−1, 𝑣𝑛}, and {𝑣𝑛, 𝑣1}.

• Wheels
• We obtain wheel Wn when we add an additional vertex to the cycle Cn, for 𝑛 ≥ 3, and then
we connect this new vertex to each of 𝑛 vertices in Cn by new edges.

• n-Cube
• The n-dimensional hypercube, or n-cube, denoted by Qn, is the graph that has vertices
representing the 2n bit strings of length n.
• Two vertices are adjacent if and only if the bit strings that they represent differ in exactly
one bit position.

Graph Representations
• A graph G can be represented in two ways
• Adjacency matrix/list
• Useful when information about the vertices is more desirable than information of edges.
• Incidence matrix
• Useful when information about edges is more desirable than information of vertices
• Adjacency list/matrix is more commonly used representation.

• Adjacency List
• It is way to represent a graph without multiple edges
• It specifies the vertices that are adjacent to each vertex of the graph.
Vertex Adjacent vertices
a b, c, e
b a
c a, e, d
d c, e
e a, c, d
Adjacency List of Undirected graph G

• Adjacency List
Initial Vertex Terminal vertices
a b, c, d, e
b b, d
c a, c, e
d
e b, c, d
Adjacency List of Directed graph G

• Determine the Adjacency List for following graphs
a b c
e
d
a b
b a
c b
d a
e c
d
c d
e
b e
d

• Determine the Adjacency List for following graphs
1
2
3
4
5
6

1 2
2 1
3
1
3 2
5

6
4
2
5 4
6
6
a b c
e
d
a b
b a
c b
d a
e c
d
c d
e
b e
d

• Adjacency Matrix
• Suppose a graph G=(V,E) is a simple graph, where |V|=n
• Vertices are listed as v1, v2, . . ., vn
• Adjacency matrix A or (AG) og G, with respect to this listing of the vertices,is the 𝒏 × 𝒏
zero-one matrix with 1 as its (i,j)th entry when vi and vj are adjacent and 0 as its (i,j)th
entry when vi and vj are not adjacent.
• Or we can say adjacency matrix A =[aij], then
𝑎𝑖,𝑗 =
1
0
𝑖𝑓 𝑣𝑖, 𝑣𝑗 𝑖𝑠 𝑎𝑛 𝑒𝑑𝑔𝑒 𝑜𝑓𝑔
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 0 1
1 0
1 0
0 1
1 0
0 1
0 1
1 0
a b c d
a
b
c
d
All undirected graphs, including multi graphs and pseudographs, have symmetric adjacency matrices.
𝑛 × 𝑛

• Adjacency Matrix
• Adjacency matrix A or (AG) of directed graph G, with respect to this listing of the vertices,is
the 𝒏 × 𝒏 zero-one matrix with 1 in its (i,j)th entry if there is an edge from vi and vj ,
• we can say adjacency matrix A =[aij] of directed graph w.r.t to the listing of the vertices,
then
𝑎𝑖,𝑗 =
1
0
𝑖𝑓 𝑣𝑖, 𝑣𝑗 𝑖𝑠 𝑎𝑛 𝑒𝑑𝑔𝑒 𝑜𝑓𝑔
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
The adjacency matrix for a directed graph does not have to be symmetric, because there
may not be an edge from aj to ai when there is an edge from ai to aj.

• Determine the adjacency matrices for the following graphs
a b c
e
d
1
2
3
4
5
6
0 1
1 0
0 1 0
1 1 0
0 1
1 1
0 0
0 0 1
0 0 1
1 1 0
0 1 0
1 0 0
1 1 0
0 0 0
1 0 0
0 1 0
0 0 0
0 1 0
0 0 0
0 0 1
1 0 1
0 0 0
a b c d e
a
b
c
d
e
1 2 3 4 5 6
1
2
3
4
5
6
0 3
3 0
0 2
1 1
0 1
2 1
1 2
2 0
a b c d
a
b
c
d

• Incidence Matrix
• Let G=(V,E) is an undirected graph,
• Suppose 𝑣1, 𝑣2, . . . , 𝑣𝑛 are the vertices and 𝑒1, 𝑒2, . . . , 𝑒𝑚 are the edges of G.
• The incidence matrix w.r.t. this ordering of V and E is the 𝒏 × 𝒎 matrix 𝑀 = 𝑚𝑖𝑗 ,
where
𝑚𝑖𝑗 =
1
0
𝑤ℎ𝑒𝑛 𝑒𝑑𝑔𝑒 𝑒𝑗 𝑖𝑠 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑡 𝑤𝑖𝑡ℎ 𝑣𝑖
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
v1 v2 v3
v4 v5
e1
e2
e3
e4 e5
e6
1 1 0
0 0 1
0 0 0
1 0 1
0 1 0
0 0 0
1 0 1
0 1 1
0 0 0
1 1 0
e1 e2 e3 e4 e5 e6
v1
v2
v3
v4
v5
𝑛 × 𝑚

• Represent the following pseudograph using Incidence Matrix
1 1 1
0 1 1
0 0 0
0 0 0
1 0 1
1 1 0
0 0 0
0 1 0
1 0 0
0 0 0 0 0 0 1 1 1
0 0 0 0 1 1 0 0 0
v1 v2 v3
v4
v5
e2
e1
e4
e3
e5
e6
e7
e8
e9
e1 e2 e3 e4 e5 e6 e7 e8 e9
v1
v2
v3
v4
v5

• Represent the following graph using Adjacency Matrix

Sub Graph
• Sometime we need only part of graph to solve a problem.
• When edges and vertices are removed from a graph, without removing
endpoints of any remaining edges, a smaller graph is obtained. Such a
graph is called a subgraph of the original graph.
• A subgraph of a graph 𝐺 = (𝑉, 𝐸) is a graph 𝐻 = (𝑊, 𝐹), where 𝑊 ⊆ 𝑉
and 𝐹 ⊆ 𝐸.
• A subgraph H of G is a proper subgraph of G if 𝐻 ≠ 𝐺
a b c
e
d
a b
d

Operations on Graphs
• Various operations can be performed on graphs
• Union (𝐴 ∪ 𝐵)
𝐴 = 2, 4, 6, 8, 10
𝐵 = 4, 8,12, 16
𝑨 ∪ 𝑩 = {𝟐, 𝟒, 𝟔, 𝟖, 𝟏𝟎, 𝟏𝟐, 𝟏𝟔}
• Intersection (𝐴 ∩ 𝐵)
𝐴 = 2, 4, 6, 8, 10
𝐵 = 4, 8,12, 16
𝑨 ∩ 𝑩 = {𝟒, 𝟖}
• Sum (𝐴 + 𝐵)
• Ring sum (𝐴⨁𝐵)
• Product (𝐴 × 𝐵)

Union of Graphs
• Two or more graphs can be combined in various ways. The new graph that contains all the
vertices and edges of these graphs is called the union of the graphs.
• The union of two simple graphs 𝐺1 = (𝑉1, 𝐸1) and 𝐺2 = (𝑉2, 𝐸2) is the simple graph
with vertex set 𝑽𝟏 ∪ 𝑽𝟐 and edge set 𝑬𝟏 ∪ 𝑬𝟐 . The union of 𝐺1 and 𝐺2 is denoted by
𝐺1 ∪ 𝐺2.
𝑉1 = 𝑎, 𝑏, 𝑐, 𝑑 , 𝑉2 = 𝑎, 𝑏, 𝑐, 𝑑, 𝑒
𝑽𝟏 ∪ 𝑽𝟐 = {𝒂, 𝒃, 𝒄, 𝒅, 𝒆}
𝐸1 = 𝑎, 𝑏 , 𝑎, 𝑑 , 𝑏, 𝑐 , 𝐸2 = 𝑎, 𝑑 , 𝑎, 𝑒 , 𝑏, 𝑑 , 𝑏, 𝑒 , 𝑐, 𝑒
𝑬𝟏 ∪ 𝑬𝟐 = { 𝒂, 𝒃 , 𝒂, 𝒅 , 𝒂, 𝒆 , 𝒃, 𝒄 , 𝒃, 𝒅 , 𝒃, 𝒆 , 𝒄, 𝒆 }
a b c
d
a b c
e
d
G1 G2

Intersection of Graphs
• The intersection of two simple graphs 𝐺1 = (𝑉1, 𝐸1) and 𝐺2 = (𝑉2, 𝐸2) is the simple
graph with vertex set 𝑽𝟏 ∩ 𝑽𝟐 and edge set 𝑬𝟏 ∩ 𝑬𝟐 . The intersection of 𝐺1 and 𝐺2 is
denoted by 𝐺1 ∩ 𝐺2. Contains common vertices and common edges of these graphs.
𝑉1 = 1,2,3,4,5,6 , 𝑉2 = 1,2,3,4,5,6
𝑽𝟏 ∩ 𝑽𝟐 = {𝟏, 𝟐, 𝟑, 𝟒, 𝟓, 𝟔}
𝐸1 = 1,2 , 1,5 , 1,6 , 2,3 , 2,6 , 3,4 , 3,6 , 4,5 , 4,6 , 5,1 , {5,6}
𝐸2 = 1,6 , 2,6 , 3,6 , 4,6 , 5,6
𝑬𝟏 ∩ 𝑬𝟐 = { 1,6 , 2,6 , 3,6 , 4,6 , 5,6 }
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
∩
G1
G2
G1 ∩ G2

Sum of Graphs
• If two simple graphs 𝐺1 = (𝑉1, 𝐸1) and 𝐺2 = (𝑉2, 𝐸2), such that 𝑉1 ∩ 𝑉2 = ∅,
then the sum of 𝐺1 and 𝐺2 is denoted by 𝑮𝟏 + 𝑮𝟐 consists of those edges, which
are in G1 and in G2 and the edges obtained, by joining each vertex of G1 to each
vertex of G2.
𝑉1 = 𝑎, 𝑏 , 𝑉2 = 𝑥, 𝑦, 𝑧
𝑽𝟏 + 𝑽𝟐 = {𝒂, 𝒃, 𝒙, 𝒚, 𝒛}
𝐸1 = 𝑎, 𝑏 , 𝐸2 = { 𝑥, 𝑦 , 𝑦, 𝑧 }
𝑬𝟏 + 𝑬𝟐 = { 𝒂, 𝒃 , 𝒂, 𝒙 , 𝒂, 𝒚 , 𝒂, 𝒛 , 𝒃, 𝒙 , 𝒃, 𝒚 , 𝒃, 𝒛 , 𝒙, 𝒚 , {𝒚, 𝒛}}
a
b
x
y
z
a
b
y
z
x
+
G1 G2
G1 + G2

Ring sum of Graphs
• The ring sum of two simple graphs 𝐺1 = (𝑉1, 𝐸1) and 𝐺2 = (𝑉2, 𝐸2) is the
simple graph with vertex set 𝑽𝟏 ∪ 𝑽𝟐 and the edges that are either in 𝐺1 or in
𝐺2 but not in both i.e. (𝑬𝟏 ∪ 𝑬𝟐) − (𝑬𝟏 ∩ 𝑬𝟐) . The ring sum of 𝐺1 and 𝐺2 is
denoted by 𝐺1⨁𝐺2.
𝑉1 = 𝑎, 𝑏, 𝑐, 𝑑, 𝑒 , 𝑉2 = 𝑏, 𝑐, 𝑑, 𝑒
𝑽𝟏 ∪ 𝑽𝟐 = {𝒂, 𝒃, 𝒄, 𝒅, 𝒆}
𝐸1 = 𝑎, 𝑏 , 𝑎, 𝑐 , 𝑎, 𝑑 , 𝑎, 𝑒 , 𝑏, 𝑐 , 𝑏, 𝑑 , 𝑏, 𝑒 , 𝑐, 𝑑 , {𝑑, 𝑒}
𝐸2 = { 𝑏, 𝑐 , 𝑏, 𝑒 , 𝑐, 𝑑 , 𝑐, 𝑒 , {𝑑, 𝑒}}
(𝑬𝟏 ∪ 𝑬𝟐) − (𝑬𝟏 ∩ 𝑬𝟐) = { 𝒂, 𝒃 , 𝒂, 𝒄 , 𝒂, 𝒅 , 𝒂, 𝒆 , 𝒃, 𝒅 , {𝒄, 𝒆}}
a
b
c d
e b
c d
e
a
b e
c d
a
b e
⨁
G1 G2 G1 ⨁ G2

Product of Graphs
• Product of sets A and B
𝐴 = 1, 2, 3, 4, 5 , 𝐵 = {9, 8, 7}
𝐴 × 𝐵 = { 1,9 , 1,8 , 1, 7 , 2,9 , 2, 8 , 2, 7 , . . . , 5, 9 , 5,8 , 5,7 }
• Product of graphs G and H
• Vertex set of G is 𝑉 𝐺 = {𝑣1, 𝑣2, . . . , 𝑣𝑛}
• Vertex set of H is 𝑉 𝐻 = {𝑢1, 𝑢2, . . . , 𝑢𝑚}
• Product of G and H i.e. V 𝐺 × 𝐻 = { 𝑣𝑖, 𝑢𝑗 𝑣𝑖 ∈ 𝑉 𝐺 𝑎𝑛𝑑 𝑢𝑗 ∈ 𝑉(𝐻)}
𝑉 𝐺 × 𝐻 = { 𝑣1, 𝑢1 , 𝑣1, 𝑢2 , . . . , 𝑣1, 𝑢𝑚 , 𝑣2, 𝑢1 , 𝑣2, 𝑢2 , . . . , 𝑣2, 𝑢𝑚 , . . . , 𝑣𝑛, 𝑢1 , 𝑣𝑛, 𝑢2 , . . . , 𝑣𝑛, 𝑢𝑚 }
• Two vertices 𝑣𝑖, 𝑢𝑗 𝑎𝑛𝑑 𝑣𝑘, 𝑢𝑙 are adjacent in 𝐺 × 𝐻 if and only if either
𝑣𝑖 = 𝑣𝑘 𝑎𝑛𝑑 𝑢𝑗 is adjacent to 𝑢𝑙in H OR
𝑢𝑗 = 𝑢𝑙 𝑎𝑛𝑑 𝑣𝑖 is adjacent to 𝑣𝑘in G

Product of Graphs
×
G H G X H
𝑣𝑖, 𝑢𝑗
𝑣𝑘, 𝑢𝑙
𝑣𝑖, 𝑢𝑗
𝑣𝑘, 𝑢𝑙
𝑣𝑖 = 𝑣𝑘 𝑎𝑛𝑑 𝑢𝑗 is adjacent to 𝑢𝑙in H
OR
𝑢𝑗 = 𝑢𝑙 𝑎𝑛𝑑 𝑣𝑖 is adjacent to 𝑣𝑘in G

Product of Graphs
• Example
a
b c
1
2
3
4
(a,1)
(b,1) (c,1)
(a,2)
(b,2) (c,2)
(a,3)
(b,3) (c,3)
(a,4)
(b,4) (c,4)

Walk
• For a graph G=(V,E), a walk is defined as a sequence of alternating vertices an edges such as
𝑣0, 𝑒1, 𝑣1, 𝑒2, 𝑣2, 𝑒3, . . . 𝑣𝑛−1, 𝑒𝑛, 𝑣𝑛 where each edge 𝑒𝑖 = {𝑣𝑖−1, 𝑣𝑖}.
• The length of this walk is 𝑛.
• Walks can have repeated edges.
• A walk is considered to be Closed if the starting vertex is the same as the ending vertex, that
is 𝑣0 = 𝑣𝑛. A walk is considered Open otherwise.
Walk is denoted as abcdb.
Length of this walk is 4.
Another possible walk is abcdbce
and its length will be ?
Walk is denoted as ecfge. Length of this walk is 4.

Trail
• A trail is defined as a walk with no repeated edges.
• The length of this trail is 𝑛.
The walk can be defined as abc.
There are no repeated edges so
this walk is also a trail.
This walk must repeat at least one edge.
So this walk cannot be a trail.

Path
• A path is a sequence of edges that begins at a vertex of a graph and travels from vertex to
vertex along edges of the graph.
• For a graph G=(V,E), a path of length 𝑛 from u to v in G is a sequence of n edges 𝑒1, 𝑒2, … , 𝑒𝑛 of
G such that 𝑒1 is associated with {𝑣0, 𝑣1}, 𝑒2 is associated with {𝑣1, 𝑣2}. and so on, with 𝑒𝑛
associated with {𝑣𝑛−1, 𝑣𝑛}, where 𝑣0 = u and 𝑣𝑛 = v.
• We denote the path by its vertex sequence 𝑣0, 𝑣1, . . . , 𝑣𝑛 because listing these vertices
uniquely determines the path.
• The path is a circuit if it begins and ends at the same vertex i.e., if u = v, and has length greater
than zero.
• The path or circuit is said to pass through the vertices 𝑣0, 𝑣1, . . . , 𝑣𝑛−1 or traverse the edges
𝑒1, 𝑒2, … , 𝑒𝑛.
• A path or circuit is simple if it does not contain the same edge more than once.

Path
a,b,c,f,e,d is a simple path with length 5.
a,b,c,f,e,d,a is a circuit with length 6.
b,f,e,c,d is not a path.
a,e,f,e,b,c is not a simple path with length 4
because it contains {e,f} twice.

Graph Connectivity
• Connectivity defines whether a graph is connected or disconnected.
• An undirected graph is called connected if there is a path between every pair of
distinct vertices of the graph.
• From every vertex to any other vertex, there should be some path to traverse that is called
the connectivity of a graph.
• A graph with multiple disconnected vertices and edges is said to be
disconnected.
G1 G2 G3

Graph Connectivity
• A connected component of a graph G is a connected subgraph of G that is not a
proper subgraph of another connected subgraph of G.

Graph Connectivity
• Cut Vertex
• The removal of a vertex and all edges incident with it produces a sub graph with more
connected components than in the original graph. Such vertices are called cut vertices (or
articulation points).
• An edge whose removal produces a graph with more connected components than in the
original graph is called a cut edge or bridge.
• The removal of a cut vertex from a connected graph produces a subgraph that is not connected.

Graph Connectivity
• Cut Vertex
G
What are the cut vertices of G?
After removing vertex b After removing vertex c After removing vertex e
What are the cut edges of G? Cut edges of G are {a,b} and {c,e}.

Graph Connectivity
• A directed graph is strongly connected if there is a path from a to b and from b to
a whenever a and b are vertices in the graph.
• A directed graph is weakly connected if there is a path between every two
vertices in the underlying undirected graph.
• i.e. A directed graph is weakly connected if and only if there is always a path between two
vertices when the directions of the edges are disregarded.
G is strongly connected. H is weakly connected.

Isomorphism of Graphs
• Can we draw two graphs in the same way?
• The simple graphs G1=(V1,E1)and G2=(V2,E2) are isomorphic if there is one to one and
onto function f from V1 to V2 with the property that a and b are adjacent in G1 if and
only if f(a) and f(b) are adjacent in G2, for all a and b in V1. Such a function f is called an
isomorphism.
• When two simple graphs are isomorphic, there is a one-to-one correspondence between
vertices of the two graphs that preserves the adjacency relationship.
• Isomorphic simple graphs also must have the same number of edges.

• The degrees of the vertices in isomorphic simple graphs must be the same.

G G’ v5 V5’

• The number of vertices, the number of edges, and the number of vertices of each
degree are all invariants under isomorphism.
• If any of these quantities differ in two simple graphs, these graphs cannot be
isomorphic.
• However, when these invariants are the same, it does not necessarily mean
that the two graphs are isomorphic.
Q. Determine whether the graphs G and H shown below are isomorphic.
G H

G
H
• Use of adjacency matrices to show isomorphism
• Define a function f and then determine whether it is an isomorphism
f(u1)=?
deg(u1)=2 and u1 is not adjacent to any other vertex of degree 2, f(u1) must be either v6
or v4.
Assume f(u1) = v6
As u2 is adjacent to u1 , so possible f(u2) must be either v3 or v5 . Assume f(u2) = v3 .
Similarly
We get f(u3)= v4 , f(u4)= v5 , f(u5)= v1 , f(u6)= v2 .
Now we have a one-to-one correspondence between the vertex set of G and the vertex
set of H.
f(u1) = v6 , f(u2) = v3 , f(u3)= v4 , f(u4)= v5 , f(u5)= v1 , f(u6)= v2

G
H
• Use of adjacency matrices to show isomorphism
Now we have a one-to-one correspondence between the vertex set of G and the vertex
set of H.
f(u1) = v6 , f(u2) = v3 , f(u3)= v4 , f(u4)= v5 , f(u5)= v1 , f(u6)= v2
We examine the adjacency matrix of G and the adjacency matrix H with the rows and
columns labeled by the images of the corresponding vertices in G
𝐴𝐻 =
v6
v3
v4
v5
v1
v2
0 1 0
1 0 1
0 1 0
1 0 0
0 0 1
1 0 0
1 0 1
0 0 0
0 1 0
0 1 0
1 0 1
0 1 0
𝑣6 𝑣3 𝑣4 𝑣5 𝑣1 𝑣2
𝐴𝐺 =
u1
u2
u3
u4
u5
u6
0 1 0
1 0 1
0 1 0
1 0 0
0 0 1
1 0 0
1 0 1
0 0 0
0 1 0
0 1 0
1 0 1
0 1 0
𝑢1 𝑢2 𝑢3 𝑢4 𝑢5 𝑢6
Because AG = AH , it follows that f preserves edges.
We conclude that f is an isomorphism, so G and H are isomorphic.

• Points to remember for checking isomorphism
Match the
• Number of vertices
• number of edges
• Number of vertices of each degree
• Degree of selected vertex and degree of its adjacent vertices.

Homomorphism of Graphs
• Two graphs G1 and G2 are said to be homomorphic, if each of these graphs can be obtained
from the same graph 'G' by dividing some edges of G with more vertices.

Homomorphism of Graphs
• Two homomorphic graphs G1 and G2 can be obtained from the same graph 'G' by
• Inserting vertices OR
• Removing vertices with degree 2

Euler and Hamiltonian Graphs
• Can we travel along the edges of a graph starting at a vertex and returning to it by traversing
each edge of the graph exactly once?
Euler Circuit
• Can we travel along the edges of a graph starting at a vertex and returning to it while visiting
each vertex of the graph exactly once?
Hamiltonian Circuit

• Euler Path and Circuit
• The town of Konigsberg, Prussia, was divided into four sections by the branches of the
Pregel River
• The townspeople wondered whether it was possible to start at some location in the town,
travel across all the bridges without crossing any bridge twice, and return to the starting
point.
• Swiss mathematician Leonhard Euler solved this problem using Graph theory

• Euler Circuit and Path
• An Euler circuit in a graph G is a simple circuit containing every edge of G.
• An Euler path in G is a simple path containing every edge of G.
G1has Euler circuit
a, e, c, d, b, a
G2 has neither
Euler circuit nor
Euler path
G3 doesn’t have Euler circuit
but it has Euler path
a, c, d, e, b, d, a, b

Which of the following directed graphs have an Euler circuit? Of those that do not, which have an Euler path?

• Necessary And Sufficient Conditions For Euler Circuits And Paths
• A connected multigraph with at least two vertices has an Euler circuit if and only if each
of its vertices has even degree
• A connected multigraph has an Euler path but not an Euler circuit if and only if it has
exactly two vertices of odd degree.
c d
b e
a
Euler Circuit: a, c, e, a, b, d, a
deg(a)=4, deg(c)=2, deg(e)=2,
deg(b)=2, deg(d)=2
Euler path: b, a, g, f, e, d, c, g, b, c, f, d
deg(a)=2, deg(b)=3, deg(c)=4,
deg(d)=3, deg(e)=2, deg(f)=4, deg(g)=4

• Hamilton Path and Circuit
• A simple path in a graph G that passes through every vertex exactly once is called a
Hamilton path.
• A simple circuit in a graph G that passes through every vertex exactly once is called a
Hamilton circuit.
Hamilton path:
a, b, d, c
a, b, c, d
Hamilton Circuit:
a, b, c, d, e, a
Neither Hamilton circuit nor
Hamilton path

• A graph with a vertex of degree one cannot have a Hamilton circuit,
• Because in a Hamilton circuit, each vertex is incident with two edges in the circuit.
• When a Hamilton circuit is being constructed and this circuit has already passed through
a vertex, then all remaining edges incident with this vertex, other than the two used
in the circuit, can be removed from consideration.
• A Hamilton circuit cannot contain a smaller circuit within it.

• 𝐾𝑛 has a Hamilton circuit whenever 𝑛 ≥ 3.
• If 𝐺 is a simple graph with 𝑛 vertices with 𝑛 ≥ 3 such that the degree of every vertex in
G is at least 𝒏
𝟐, then G has a Hamilton circuit.
• If is G a simple graph with n vertices with 𝑛 ≥ 3 such that 𝒅𝒆𝒈 𝒖 + 𝒅𝒆𝒈(𝒗) ≥ 𝒏 for
every pair of non-adjacent vertices u and v in G, then G has a Hamilton circuit.

Planar Graphs
• Can we join these houses and utilities so that none of the connections cross?
• When is it possible to find at least one way to represent this graph in a plane without any
edges crossing?

Planar Graphs
• A graph is called planar if it can be drawn in the plane without any edges crossing
• Where a crossing of edges is the intersection of the lines or arcs representing them at a point
other than their common endpoint.
• Such a drawing is called a planar representation of the graph.
• A graph may be planar even if it is usually drawn with crossings, because it may
be possible to draw it in a different way without crossings.
a b
c
d
a b
c
d
a b
d c
e f
g
h
a b
c
d
e f
g
h
a b
c
e f
g
h
d

Planar Graphs

Planar Graphs
v6
• As last vertex v6 is not possible to connect without crossing some edge, So given graph is not a
planar graph.
• The three houses and three utilities cannot be connected in the plane without a crossing.

Planar Graphs
• Euler’s Formula
• A planar representation of a graph splits the plane into regions, including an unbounded
region.
• Euler showed that all planar representations of a graph split the plane into the same
number of regions.
• Let G be a connected planar simple graph with e edges and v vertices. Let r be the
number of regions in a planar representation of G. Then
𝒓 = 𝒆 − 𝒗 + 𝟐
• Proof of theorem can be given by constructing a sequence of subgraphs G1, G2, ... , Ge = G,
1
2
R1
1
2
R1
3
1
2
R1
3
R2
1
2
R1
3
R2
4
5
R3
1
2
R1
3
R2
4
5
R3
R4
G1 G2 G3 G4 G5

Planar Graphs
Suppose that a connected planar simple graph has 24 vertices, each of degree 3. Into how
many regions does a representation of this planar graph split the plane?

Planar Graphs
• Corollary1
• If G is a connected planar simple graph with e edges and v vertices, where 𝑣 ≥ 3, then
𝑒 ≤ 3𝑣 − 6
• Corollary2
• If G is a connected planar simple graph, then G has a vertex of degree not exceeding five.
If G has at least three vertices,
we know that 𝑒 ≤ 3𝑣 − 6 (from Corollary 1),
so 2𝑒 ≤ 6𝑣 − 12
If the degree of every vertex were at least six,
then because 2𝑒 = 𝑣∈𝑉 deg(𝑣), we will have
2𝑒 ≥ 6𝑣
But this contradicts the inequality 2𝑒 ≤ 6𝑣 − 12
It follows that there must be a vertex with degree no greater than five.

Planar Graphs
• Degree of a region
• The number of edges on the boundary of this region.
• When an edge occurs twice on the boundary, it contributes two to the degree.

Planar Graphs
• Corollary1
• If G is a connected planar simple graph with e edges and v vertices,
where 𝑣 ≥ 3, then
𝑒 ≤ 3𝑣 − 6
• Proof
• A connected planar simple graph drawn in the plane divides the plane
into r regions
• The degree of each region is at least three
• Sum of the degrees of the regions is exactly twice the number of
edges in the graph.
• Because each region has degree greater than or equal to three, it
follows that
2𝑒 =
𝑎𝑙𝑙 𝑟𝑒𝑔𝑖𝑜𝑛𝑠 𝑅
deg(𝑅) ≥ 3𝑟
• Hence
2
3 𝑒 ≥ 𝑟

Planar Graphs
• Proof to Corollary1
𝑒 − 𝑣 + 2 ≤ 2
3 𝑒
It follows that
𝑒
3 ≤ 𝑣 − 2
This shows that
𝑒 ≤ 3𝑣 − 6
𝑏𝑦 𝑢𝑠𝑖𝑛𝑔 𝐸𝑢𝑙𝑒𝑟′
𝑠 𝐹𝑜𝑟𝑚𝑢𝑙𝑎 𝑟 = 𝑒 − 𝑣 + 2

Planar Graphs
• Corollary1
• If G is a connected planar simple graph with e edges and v vertices, where 𝑣 ≥ 3, then
𝑒 ≤ 3𝑣 − 6
• Corollary2
• If G is a connected planar simple graph, then G has a vertex of degree not exceeding five.

Revise Graph Connectivity
• Cut Vertex
• The removal of a vertex and all edges incident with it produces a sub graph with more
connected components than in the original graph. Such vertices are called cut vertices (or
articulation points).
• An edge whose removal produces a graph with more connected components than in the
original graph is called a cut edge or bridge.
• The removal of a cut vertex from a connected graph produces a subgraph that is not connected.

Graph Connectivity
• Cut set
• A cut set of a graph is a set of edges such that the removal of these edges produces a
subgraph with more connected components than in the original graph, but no proper subset
of this set of edges has this property.
Cut set E1 = {e1, e8, e5, e3} Cut set E2 = {e3, e6, e2}

Graph Theory

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