Graph theory and its applications

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APPLICATIONS OF GRAPH THEORY
A PROJECT REPORT
Submitted
In partial fulfilment of the requirements for the award of degree
Master of Science
In
Mathematics
By
S.MANIKANTA
(HT.NO:1683531014)
Under the esteemed guidance of
A.PADHMA
Department of Mathematics
GOVERNAMENT COLLEGE (A), RAJAMAHENDRAVARAM
Affiliated by AKNU, Rajamahendravaram
Andhra Pradesh, India
2017-2018

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Certificate
This is to certify that the project entitled
“APPLICATIONS OF GRAPH THEORY” is the bonafide
work carried out by S.MANIKANTA during the academic
year 2017-18 in partial fulfilment of the requirements for
the award of the degree of master of science in dept. of
mathematics , Government(A) College ,
Rajamahendravaram .
External Examiner
Signature of the Guide signature of HOD
The matter embodied in this project work has not been submitted earlier
for award of any degree or diploma to the best of my knowledge and
belief. Certified that the above mentioned project has been duly carried
out as per the norms of the college and statutes of the university

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Acknowledgements
I wish to express my deep sense of thanks and gratitude to
A.PADHMA, Department MATHEMATICS, Government (A)
College, who guided me in intricacies of this project.
I would like to offer my special thanks to DR.CH.SRINIVASULU,
Head of the Dept (MATHEMATICS), Government (A) College For his
co-operation and support during project preparation.
I am particularly grateful to Dr. Rapaka David Kumar Swamy,
principal, Government (A) College, for the guidelines given by him.
My special thanks to Sri N. Kiran Kumar, principal, V.SM College of
RCPM for his valuable and constructive suggestions and assistance for
development of this work

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CONTENTS
Certificate 2
Acknowledgements 3
1. Graph theory 7-9
1.1. History
1.2. Definition
2. Terminology 9-12
2.1. Loop
2.2. Multigraph
2.3. Directed and Un-directed graph
2.4. Pseudo graph
2.5. Simple graph
2.6. Finite and Infinite graphs
2.7. Degree of vertex
2.8. Isolated and pendent vertices
3. Types of Graphs 12-15
3.1. Null graph
3.2. Complete graph
3.3. Regular graph
3.4. Cycles
3.5. Wheels
3.6. Platonic graph
3.7. N-Cube
4. Tree and Forest 15-16
4.1. Tree
4.2. Forest
4.3. Poly tree
4.4. Types of trees
5. Graph isomorphism and Graph operations 17-23
5.1. Graph isomorphism
5.2. Complex graph operations
5.2.1. Union
5.2.2. Sum of two graphs

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5.2.3. Intersection
5.2.4. Graph join
5.2.5. Difference of graphs
5.2.6. Graph compliment
5.2.7. Power graph
6. Walks, paths and circuits 24-25
6.1. Walk
6.2. Closed walk
6.3. Trail
6.4. Circuit
6.5. Path
6.6. Cycle
6.7. Connected graph
6.8. Component
7. Representation of graphs as Matrix 25-26
7.1. Adjacency matrix
7.2. Incidence matrix
8. Graph in Circuits 26-30
8.1. Tree
8.2. Matrix
8.2.1. Incident matrix
8.2.2. Loop matrix or Tie set matrix
8.2.3. Cut set matrix
9. Applications in Computer science 31-33
9.1. Network
9.2. Webpage
9.3. Workflow
9.4. Neural networks
9.5. Google maps
10. Fingerprint Recognition using Graph Representation
33-37
10.1. Finger print types
10.2. Minute, core and delta
10.3. Different classifications
10.4. Old method, New method and Process
10.5. Constriction of related weight graph
10.5.1. Some information can be used in constructing the graph
related to a finger print like
10.5.2. Weight-age to Nodes and Edges

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11. Graph theory models in security 38-40
11.1. The four-color graph theorem
11.1.1. Applying of the four color theorem in wireless a cell tower
placement plan.
11.1.2. Node coloring theorem
12. Network coding 40-42
13. Chemical graph theory 42-44
13.1. Definitions
13.2. Molecular energy
13.3. Graph nullity and zero-Energy states
14. Recent applications of graph theory in molecular
biology 45-48
14.1. New applications in molecular biology
14.2. New field has recently emerged called bioinformatics –
application of IT and CS to molecular biology.
15. Knight’s Tour 47-50
16. Common Problem 51-54
16.1. House of Santa Claus
16.2. Three utilities problem
16.3. Seven Bridges of Königsberg
16.4. Shortest path problem
16.5. Travelling salesman problem(TSP)
17. Bibliography 55

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1 Graph theory
In mathematics, graph theory is the study of graphs, which are
mathematical structures, used to model pair wise relations between objects.
A graph in this context is made up of vertices, nodes, or points which are
connected by edges, arcs, or lines. A graph may be undirected, meaning
that there is no distinction between the two vertices associated with each
edge, or its edges may be directed from one vertex to another
1.1 History
The Konigsberg Bridge problem
In the present century, there have already been a great many rediscoveries
of graph theory which can only mention most briefly.
Euler (1707-1782) becomes the father of graph theory as well as Topology.
The paper written by Leonhard Euler on the Seven Bridges of Konigsberg
and published in 1736 is regarded as the first paper in the history of graph
theory. This paper, as well as the one written by Vandermonde on the knight
problem, carried on with the analysis situs initiated by Leibniz. Euler's
formula relating the number of edges, vertices, and faces of a convex
polyhedron was studied and generalized by Cauchy and L'Huilier, and
represents the beginning of the branch of mathematics known as topology.

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More than one century after Euler's paper on the bridges of Konigsberg and
while Johann Benedict Listing was introducing the concept of topology,
Cayley was led by an interest in particular analytical forms arising from
differential calculus to study a particular class of graphs, the trees. This
study had many implications for theoretical chemistry.
1.2 Definitions
There is large number of definitions in graph theory. The following are
some of the more basic ways of defining graphs and related mathematical
structure
Graph
a graph is an ordered pair G = (V, E) consisting of a non-empty set
V={v1,v2,v3,...} of vertices or nodes or points together with a set
E={e1,e2,e3,...} of edges or arcs or lines, which are 2-element subsets of V
(i.e. an edge is associated with two vertices, and that association takes the
form of the unordered pair comprising those two vertices) in other words A
graph G is a collection of vertices “V” and edges “E”.So we say G = (V, E)
is graph
The set V(G) is called the vertex set of G and E(G) is called the edge set.
Usually the Graph is denoted by G=(V,E) for u,vϵV set and {u,v} an edge
of G. Since {u,v} is 2-elements set, we may write {v,u}.

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If e=uv is an edge of G, then we say that u and v are adjacent in G and e
joins u and v.
For example:
A graph G is denoted by the sets
V(G)={ u, v, w, x, ,z} and E(G)={ uv, uw, wx, xy, xz}.
Now we have the following graph by considering these sets.
Every graph as a diagram associated with it.
If two distinct edges say e1 and e2 are incident with common vertex, then
they are adjacent edges.
A graph with p-vertices and q-edges is called a (p,q) graph.
The (1, 0) graph is called trivial graph.
2 TERMINOLOGY
2.1 Loop: an edge of the graph that joins a node to itself is called
loop or self-loop i.e., a loop is an edge (vi, vj) where vi = vj.
2.2 Multigraph: In a multigraph no loops are allowed but more
than one edge can join two vertices, these edges are called
multiple edges or parallel edges and graph is called multigraph

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2.3 Directed & Un-directed graph: If each edge of graph G
has a direction then the graph is called Directed Graph
(Example of directed graph fig.1). In Directed Graph each edge
is represented by an arrow or direction curve from initial point u
of e to the terminal point v (fig.2). If each edge of G has no-
direction then the graph is called as An Un--Directed Graph.
2.4 Pseudo graph: a graph with loops and multiple edges are
allowed, is called a pseudo graph
2.5 Simple graph: a graph which has neither loops nor multiple
edges
2.6 Finite and Infinite graphs: a graph with finite number of
vertices as well as a finite number of edges is called finite graph.
Otherwise infinite graph.
2.7 Degree of vertex: The number of edges incident on a vertex vj
with self-loops is called the degree of a vertex vj and denoted by
degG(vj) or deg vj or d(vj).
2.8 Isolated and Pendent vertices: A vertex has no incident
edge is called an Isolated vertex. In other words, isolated
vertices are those with zero degree. A vertex of degree one is
called Pendent vertex or End vertex.

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1. Statement: (Handshaking theorem)
If G = (V, E) be an undirected graph with e edges.
Then G (v) =2e
i.e., the sum of the degree of the vertices in an undirected
graph is even.
2. Statement: The number of vertices of odd degree in a graph is
always even
Problem 2.1. Determine the number of edges in a graph with 6
vertices, 2 of degree 4 and 4 of degree 2. Draw two such graphs.
Solution. Suppose the graph with 6 vertices has e number of edges.
Therefore, by Handshaking lemma
(vi) = 2e
═> d(v1) + d(v2) + d(v3) + d(v4) + d(v5) + d(v6) = 2e
Now, given 2 vertices are of degree 4 and 4 vertices are of degree 2.
Hence the above equation,
(4 + 4) + (2 + 2 + 2 + 2) =2e
16 =2e ═> e = 8
Hence the number of edges in the graph with 6 vertices with given condition
is 8. Two such graphs are shown below in figure 7.

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Problem 2.2. Draw the graphs of the chemical molecules of
(i) Methane (CH4) (ii) Propane (C3H8)
3 Types of Graphs
3.1 Null Graph: A graph which contains only isolated node, is
called a null graph
i.e., the set of edges in the graph is empty.
Null graph is denoted on n vertices by Nn
N4 is shown in the finger 8.
Fig.8

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3.2 Complete Graph: A simple graph G is said to be complete
if every vertex in G is connected with every other vertex.
i.e., If G contains exactly one edge between each pair of distinct
vertices.
A complete graph is usually denoted by Kn. It should be noted that
Kn has exactly edges.
The graph Kn for n= 1, 2, 3,4,5,6 are shown in Fig 9.
3.3 Regular Graph: A graph, in which all the vertices are of
Equal degree, is called a Regular Graph.
If the degree of each vertex is r, then the graph is called a regular
graph of degree r.
3.4 Cycles: The cycle Cn, consist of n vertices v1 ,v2 , ……, vn and
edges [v1 ,v2 ], [v2 ,v3 ], [v3 ,v4 ],….., [vn-1 ,vn ].
The cycles C3 ,C4 ,C5 and C6 are shown in Fig 10.
3.5 Wheels: The wheel Wn is obtained when an additional vertex to
the cycle Cn, for n≥3, and connect this new vertex to each of the n
vertices in Cn, by new edges. The wheels W3, W4, W5 and W6 are

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displayed in the Fig 11.
3.6 Platonic Graph: the graph formed by edges and vertices of
five regular(platonic) solids-The tetrahedron, octahedron, cube,
dodecahedron and icosahedrons.
3.7 N-cube : The N-cube denoted by Qn, is the graph that has
vertices representing the 2n
bit strings of length n. The adjacent if
and only if the bit strings that they represent differ in exactly one
bit position. The graphs Q1 ,Q2 ,Q3 are displayed in the figure 13.
Thus Qnhas 2n
vertices and n.2n-1
edges, and is regular of degree n.

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4 Tree and Forest
4.1 Tree
A tree is an undirected graph G that satisfies any of the following equivalent
conditions:
 G is connected and has no cycles.
 G is acyclic, and a simple cycle is formed if any edge is added to G.
 G is connected, but is not connected if any single edge is removed
from G.
 G is connected and the 3-vertex complete graph K3 is not a minor of G.
 Any two vertices in G can be connected by a unique simple path.
If G has finitely many vertices, say n of them, then the above statements are
also equivalent to any of the following conditions:
 G is connected and has n − 1 edges.
 G has no simple cycles and has n − 1 edges.
As elsewhere in graph theory, the order-zero graph (graph with no vertices)
is generally excluded from consideration: while it is vacuously connected as
a graph (any two vertices can be connected by a path), it is not 0-
connected (or even (−1)-connected) in algebraic topology, unlike non-empty
trees, and violates the "one more vertex than edges" relation.
An internal vertex (or inner vertex or branch vertex) is a vertex
of degree at least 2. Similarly, an external vertex (or outer vertex, terminal
vertex or leaf) is a vertex of degree 1.
An irreducible tree (or series-reduced tree) is a tree in which there is no
vertex of degree 2.

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4.2 Forest
A forest is an undirected graph, all of whose connected components are
trees; in other words, the graph consists of a disjoint union of trees.
Equivalently, a forest is an undirected acyclic graph. As special cases, an
empty graph, a single tree, and the discrete graph on a set of vertices (that
is, the graph with these vertices that has no edges), are examples of forests.
Since for every tree V - E = 1, we can easily count the number of trees that
are within a forest by subtracting the difference between total vertices and
total edges. TV - TE = number of trees in a forest.
4.3 Poly tree
A polytree[11]
(or oriented tree[4A][4B]
or singly connected network[4C]
) is
a directed acyclic graph (DAG) whose underlying undirected graph is a tree.
In other words, if we replace its directed edges with undirected edges, we
obtain an undirected graph that is both connected and acyclic.
A directed tree is a directed graph which would be a tree if the directions on
the edges were ignored, i.e. a polytree. Some authors restrict the phrase to
the case where the edges are all directed towards a particular vertex, or all
directed away from a particular vertex (see arborescence)
4.4 Types of Trees
 A path graph (or linear graph) consists of n vertices arranged in a
line, so that vertices i and i+1 are connected by an edge
for i=1,…,n−1.
 A starlike tree consists of a central vertex called root and several
path graphs attached to it. More formally, a tree is starlike if it has
exactly one vertex of degree greater than 2.
 A star tree is a tree which consists of a single internal vertex
(and n−1 leaves). In other words, a star tree of order n is a tree of
order n with as many leaves as possible.
 A caterpillar tree is a tree in which all vertices are within distance 1
of a central path subgraph.
 A lobster tree is a tree in which all vertices are within distance 2 of a
central path subgraph.

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5 GRAPH ISOMORPHISM and GRAPH
OPERATIONS:
5.1 Graph isomorphism:
Two graphs G and G' are said to be isomorphic to each other if
there is a one-to-one correspondence (bijection) between their vertices
and between their edges such that the incidence relationship is
preserved.
Correspondence of vertices Correspondence of edges
f(a) = v1 f(1) = e1
f(b) = v2 f(2) = e2
f(c) = v3 f(3) = e3
f(d) = v4 f(4) = e4
f(e) = v5 f(5) = e5
Adjacency also preserved. Therefore G and G' are said to be isomorphic.
The following graphs are isomorphic to each other. i.e two different ways of
drawing the same graph
.

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The following three graphs are isomorphic.
The following two graphs are not isomorphic, because x is adjacent to two
pendent vertex is not preserved.
5.2 Complex Graph Operations:
5.2.1Union :given two graphs G1 and G2, their union will be a graph
such that V(G1 G2 ) = V(G1) V(G2)
And E (G1 G2 ) =E(G1) E(G2)

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The union of two graphs G(VG, EG) and H(VH, EH) is the union of their
vertex sets and their edge families. Which means G H = (VG VH, EG
EH).
The below image shows a union of graph G and graph H. Make sure that all
the vertices and edges from both the graphs are present in the union. If
possible draw it once yourself.
5.2.2Sum of two graphs: In graph theory, a branch of mathematics,
the disjoint union of graphs[6B] is an operation that combines
two or more graphs to form a larger graph. It is analogous to
the disjoint union of sets, and is constructed by making the vertex
set of the result be the disjoint union of the vertex sets of the given
graphs, and by making the edge set of the result be the disjoint
union of the edge sets of the given graphs. Any disjoint union of
two or more nonempty graphs is necessarily disconnected. the

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union where the VG and VH are disjoint. In this case the Union is
referred to as disjoint Union and it is denoted by G + H .
5.2.3Intersection: The intersection of two graphs G(VG, EG) and
H(VH, EH) is the union of their vertex sets and the intersection of
their edge families. Which means G ∩ H = (VG VH, EG ∩ EH).
Below image demonstrate the Intersection of the two Graphs.
Notice that the vertex set is a union of both the vertex sets but the edge
family consists only the edges which exist in both the graphs G and H.
5.2.4Graph join: Given two graphs G(VG, EG) and H(VH, EH), the
join Graph will contain the union of the two graphs and all edges
which can connect the vertices of G to vertices of H.
Below is an image to demonstrate that.

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Note that the join graph
contains new edges (which
join the vertices of G to
vertices of H) apart from
the disjoint union.
5.2.5Difference of Graphs: Given two graphs G(VG, EG) and H(VH,
EH), the difference Graph G – H will contain the union of the
vertices of two graphs G and H and the edges in the difference is
the difference of edges which is EG – EH
Few points
 The Graph Difference of any graph and itself is an empty graph. Which
means G – G = Empty Graph.
 The vertices of the Graph Difference (G – H) are the union of the
vertices of the graphs G and H
 The edges of the Graph Difference (G – H) are the complement of the
edges of the graphs G and H.
 The Graph Difference (G – H) of two graphs has the same vertices as
Graph Union (G H)
 The Graph Difference (G – H) of two graphs has the same vertices as
Graph Intersection G ∩ H
Below is an image to demonstrate the Graph Difference (union of the
two vertices)

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Note that the vertex set is the union of the two vertex sets where as the
edges in the difference is the family of edges which are present in G but
not in H.
5.2.6Graph Compliment: The complement of graph G(V, E) is a
graph that has the same vertices as G but the edges defined by two
vertices in the complement is adjacent only if they are not adjacent
in G(V, E).
Few points
 The Graph complement G’ is also called edge complement.
 Vertex set is same for both G and G’.
 Edges in G’ are not present in G.
 The union of G and G’ is a complete graph.
 The complement of a complete Graph is a Null Graph.
Below is a demonstration of Graph Complement. Note that the edges in G
are not present in G’
5.2.7Power Graph: The Power Graph of Graph G(V, E) is always
calculated with respect to a constant k. Which means we will
always say that the kth
power of G and not just power of G. The
kth
power of G is a graph with the same set of vertices as G and an
edge between two vertices in the Power Graph exists only if there
is a path of length at most k between them.

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Consider the below graph G(V, E), and let’s say that we need to derive its
square, which means the value of k is 2.
So the graph will result into the graph on the right.
Note that the vertices 1 and 3 are connected because the there exists a path
of length two or less in the original graph..
Similarly there exists paths of length two or less between {2, 4}, {3, 5}, {4,
1}, {5, 2 }, hence these vertices are connected in the power graph.
This concludes that all the graph are the first power of themselves. Which
means if value of k is 1 then we get the same graph? And if the adjacency
matrix of G is A then each cell (i,j) of A1
will give you the number of paths
of length 1 between i and j.
Lets take another example. Again let the value of k is 2. Consider the graph
below.
The edges {1,3}, {2,3}, {3,4}, {4,6}, {5,1} and {6,2} are connected
because they have a path length of 2 or less between them in the original
graph.
The edges {1,4}, {2,5} and {3,6} are not connected because the length of
the path between them is three which is more than two.

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6 WALKS, PATHS, CIRCUITS
6.1 Walk: A walk is defined as a finite alternating sequence of
vertices and edges, beginning and ending with vertices. No edge
appears more than once. It is also called as an edge train or a
chain.
Walk “x to y” is W = {xe1v1e2v2…vn-1eny} a sequence of vertices
and edges.
6.2 Closed Walk: closed walk occurs when x=y(in above fig)
i.e., a walk from vertex v to the same v.
6.3 Trail : a trail is a walk with no repeated edges
A walk from a to f = abcf , adcf ,adecf and abdcf are trails.
A walk from a to f = adbdcf is not a trail.
6.4 Circuit: A closed walk in which no edge appears more than
once is called a circuit. That is, a circuit is a closed trail.
A walk from a to a = adeba ,adcba ,abceda and adb are circuits.

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6.5 Path: An open walk in which no vertex appears more than once
is called path. The number of edges in the path is called length of
a path.
6.6 Cycle: A closed path is called as cycle
6.7 Connected graph: A graph G is said to be connected if there
is at least one path between every pair of vertices in G. Otherwise,
G is disconnected.
6.8 Component: disconnected graph consists of two or more
connected graphs. Each of these connected subgraph is called a
component.
7 Representation of graphs as Matrix:
Although a diagrammatic of a graph is very convenient for visual
study but this is only possible when the number of edges and vertices
are reasonably small.
The matrix are commonly used to represent graphs for computer
processing. The advantages of representing the graphs in matrix form
lies on the fact that many resut of matrix algebra can be readily
applied to study the structures of graphs from an algebraic point of
view.
Two types representation are given below:

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7.1 Adjacency matrix: To any graph G there corresponds v
v matrix A(G) = [ aij ], in which aij is the number of edges joining
vi and vj.
7.2 Incidence matrix: To any graph G there corresponds a v
e matrix called the incident matrix of G. The incidence matrix of
G is the matrix M(G) = [mij], where mij is the number of times (0,
1 or 2) that vi and ej are incident. this is a different way of
specifying the graph.
8 Graph in Circuits:
Graph: Diagram consisting Node and Branch is called graph.
Node: Inter connection of components.
Branch: Line connecting two nodes.
Circuit
Here a, b, c and d are called Nodes and ab, bc, cd, db, and ad are
Branches.

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When we convert this circuit in to graph we get
4-nodes (a, b, c, d)
6-branches.
8.1 Tree : collection of nodes without any loop
There are 2 things in trees
1: Twig: Branches of a tree are called as twigs
2: Link: Remaining branches of graph after the tree taken are called
as links
Twig: - 1, 2 and 3
Links: -4, 5 and 6

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8.2 Matrix :-
In graphs we have 3 kinds of matrices.
Here we take the values as per the direction. If the arrow is
coming towards node we take -1 and if the arrow going away from
node we take +1. if node and branch has no connection we take
zero as a element in matrix corresponding to them.
8.2.1Incident matrix:-
The incident matrix for the given graph is
[Ac] = Complete incident
matrix.
(Since every Branch consist of one -1 and one +1.)
[A]= Incident matrix. (if we delete the row d then we get the
matrix is called incident)
8.2.2Loop matrix (or) Tie set matrix:-

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Here we will consider the loops existed in the graph. We will take
those branches which leads to loops in graph. The following are the
loops occurred in graph
`
<= Loop 3
Loop 4=>
<=Loop 6
Loop 7=>
We can verify that the matrix is correct or not i.e.,
The matrix between loops and itself gives identity matrix .

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8.2.3Cut set matrix
By taking the cut set tree, we forms a matrix between branches of
cut set branches of original graph.
W
e
consider the cut set branches 1, 3, 4 and 7. Taking +1 if the
branch of a column is in the direction of branch on row. If not we
take -1 at which the node at the head of the branch is considered
and takes zero if the branch is not connected to that node.

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9 Applications in computer Science
Since computer science is not a concrete/centralized subject, we
can introduce graph theory in many areas
9.1 Networks: Graph theory can be used in computer networks,
for security purpose or to schematize network topologies, for
example.
9.2 Webpage: can be represented by a direct graph. The vertices
are the web pages available at the website and a directed edge
from page A to page B exists if and only if A contains a link to B

32
9.3 Workflow: It’s sequence of processes through which a piece
of work passes from initiation to completion. Can be also
represented as directed graph.
9.4 Neural Networks: A series of algorithms that attempt to
identify underlying relationships in a set of data by using a
process that mimics the way the human brain operates.

33
9.5 Google Maps:
10 Fingerprint Recognition using Graph
Representation
The three characteristics of FINGER
PRINTS are:
1. There are no similar fingerprints in
the world.
2. Fingerprints are unchangeable.
3. Fingerprints are one of the unique
features for identification systems.

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10.1FINGERPRINT TYPES
The lines that flow in various patterns across fingerprints are called Ridges
and the spaces between ridges are Valleys.
Types of patterns in Fingerprint.
1 and 2 are terminations.
3 is bifurcation.

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10.2MINUTE, CORE AND DELTA
Minute – The places at which the ridge intersects or ends
Core – The places where the ridges form a half circle.
Delta – The places where the ridges form a triangle.
10.3DIFFERENT CLASSIFICATION

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10.4OLD METHOD:-
NEW METHOD:-
PROCESS FLOWS :-

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10.5Construction of related weight graph
Graph can be shown by G index including four parameters of
G= (V, E, μ, υ). Where V is number of nodes. E is number of
edges.
μ is weight of nodes.υ is weight of edges.
10.5.1 Some information can be used in constructing the
graph related to a finger print like:
• Centre of gravity of regions.
• The direction related to the elements of the various regions.
• The area of all the regions.
• The distance between centres of gravity.
• The perimeter of regions.
10.5.2 Weight-age to Nodes and Edges
Wn = Area (Ri)
Where
i = 1, 2, 3… n.
Wn is the weight of nodes.
Ri is the specified region in block directional image.
We = (Adj − p) × (Node − d) × (Diff −v)
Where
Adj-p is the boundary of two adjacent regions linking with an edge.
Node-d is the distance difference between nodes that links by an edge
Diff-v is the phase difference or direction difference between two
regions of block directional image.

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11 Graph Theory Models In Security
Graph theory :
A graph is a simple geometric structure made up of vertices and
lines. The lines may be directed arcs or undirected edges, each
linking a pair of vertices. Amongst other fields, graph theory as
applied to mapping has proved to be useful in Planning Wireless
communication networks.
11.1The Four-Color Graph Theorem :
The famous four-color theorem states that for any map, such as
that of the contiguous (touching) provinces of France below, one
needs only up to four colors to color them such that no two
adjacent provinces of a common boundary have the same color.
With the aid of computers, mathematicians have been able to
prove that this applies for all maps irrespective of the boarder or
surface shape
11.1.1 Applying of the four color theorem in wireless a cell
tower placement plan.
Consider the cell tower placement map shown above, where each
cell tower broadcast channel is likened to a color, and channel–
Fig.14 Provinces of France

39
colors are limited to four, the task of finding where to
economically position broadcast towers for maximum coverage is
equitable to the four-color map problem.
The two challenges are:
1. Elimination of the no-coverage spots ( marked red in the
diagram below )
2. Allocation of a different channel in the spots where channel
overlap occurs (marked in blue). In analogy, colors must be
different, so that cell phone signals are handed off to a
different channel.
Each cell region therefore uses one control tower with a specific
channel and the region or control tower adjacent to it will use
another tower and another channel. It is not hard to see how by
using 4 channels, a node coloring algorithm can be used to
efficiently plan towers and channels in a mobile network, a very
popular method in use by mobile service providers today
11.1.2 Node Coloring Theorem
As can be seen in the map below, borders wander making it a
difficult problem to analyze a map. Instead of using a
sophisticated map with many wandering boundaries, it becomes
a simpler problem if we use node coloring. If two nodes are
connected by a line, then they can't be the same color. Wireless
Service providers employ node coloring to make an extremely
complex network map much more manageable.
Fig.15. Cellular Mobile tower placement map
coloring

40
12 Network Coding
Network coding is another technique where graph theory finds
application in mobile communication networks. In a traditional
network, nodes can only replicate or forward incoming packets.
Using network coding, however, nodes are able to algebraically
combine received packets to create new packets
Network coding opens up new possibilities in the fields of
networking. Such would include:
Wireless multi-hop networks:
-hoc networks
Fig.16(a) A Map with complex wandering
boundaries
Fig16(b). The simplified network version of the
map derived by node coloring
Fig.17. Node replication of forward incoming
packets

41
Peer-to-peer file distribution
-to-peer streaming
Application of network coding in a content distribution scenario
For this application, the following assumptions are made:
1. The network is a multicast system where all destinations wish to receive
similar information from the source.
2. That all Links have a unit capacity of a single packet per time slot
3. That the links be directed such that traffic can only flow in one
direction.
1st time slot
Following the first time slot: Destination t1 will have received
information traffic A whereas Destination t2 will have received both
traffic A and traffic B as shown
below.
2nd time slot
In the second time slot: Both Destination t1 and t2 will have
received traffic A and traffic B and C as shown below
Fig.18 Content distribution scenario
First slot 2
nd
slot EX-OR Computation

42
Finally , when Destination t1 receives A and A(EX-OR)B, it will be
able to compute B by B=A (EX-OR){ A(EX-OR)B}
Likewise, when Destination t2 receives B and A (EX-OR) B, it will
be able to compute A by: A= {A (EX-OR) B} (EX-OR) B as shown
below
Network Coding application in Opportunistic Routing
Opportunistic routing is a technique that makes use of multiple
paths in a network to obtain diversity. Network coding can be
applied in such a case (fig 9) to coordinate transmissions in order
to avoid duplicate packets
Fig 19. Shows how network coding simply helps us determine how many packets should be
sent by each node
13 Chemical Graph Theory
Chemical graph theory (CGT) is a branch of mathematical chemistry
which deals with the nontrivial applications of graph theory to solve
molecular problems. In general, a graph is used to represent a molecule
by considering the atoms as the vertices of the graph and the molecular
bonds as the edges. Then, the main goal of CGT is to use algebraic
invariants to reduce the topological structure of a molecule to a single
number which characterizes either the energy of the molecule as a
whole or its orbitals, its molecular branching, structural fragments, and
its electronic structures, among others.

43
These graph theoretic invariants are expected to correlate with physical
observables measures by experiments in a way that theoretical
predictions can be used to gain chemical insights even for not yet
existing molecules. In this brief review we shall present a selection of
results in some of the most relevant areas of CGT.
13.1DEFINITIONS
D1: A molecular graph G = (V;E) is a simple graph having n =│ V │
nodes and m = │E │ edges. The nodes vi ϵ V represent non-hydrogen
atoms and the edges (vi; vj) ϵ E represent covalent bonds between the
corresponding atoms. In particular, Hydrocarbons are formed only by
carbon and hydrogen atoms and their molecular graphs represent the
carbon skeleton of the molecule.
D2: An alternant conjugated hydrocarbon is a hydrocarbon with
alternant multiple (Double and/or triple) and single bonds, such as the
molecular graph is bipartite and the edges of the graph represents C = C
and = C − C = or C ≡ C and ≡ C − C ≡ bonds only
13.2Molecular Energy
 In the Huckel Molecular Orbital (HMO) method for
conjugated hydrocarbons the energy of the jth
molecular orbital
of the so-called -electrons is related to the graph Spectra by
Where is an eigenvalue of the adjacency matrix of the
hydrogen-depleted graph representing the conjugated
hydrocarbon and are empirical parameters [6A]
 The total (molecular) energy is given by
Where ne is the number of electrons in the molecule and gj
is the occupation number of the jth
molecular orbital.
 For neutral conjugated systems in their ground state

44
 Let G be a graph with n vertices and m edges. Then,
 Let G be a graph with m edges. Then, 2
 Let G be a graph with n vertices. Then, E 2 , where
the equality holds if G is the star graph with n vertices.
 Let G be a graph with n vertices. Then , E ;
Where the equality holds if and only if G is a strongly regular
graph with parameters (n,(n+
.
 Let G be a bipartite graph with n vertices and m edges. Then
;
 For all sufficiently large n, there is a graph G of order n such
that
13.3Graph Nullity and Zero-Energy States
The nullity of a (molecular) graph, denoted by = (G), is the
algebraic multiplicity of the number zero in the spectrum of the
adjacency matrix of the (molecular) graph.
An alternant unsaturated conjugated hydrocarbon with = 0 is
predicted to have a stable, closed-shell, electron configuration.
Otherwise, the respective molecule is predicted to have an
unstable, open-shell, electron configuration.
If n is even, then is either zero or it is an even positive integer.

45
14 Recent Applications Of Graph Theory
In Molecular Biology
14.1New applications in molecular biology
 Graphs as Secondary RNA structures
 Graphs as Amino Acids
 Graphs as Proteins
Amino Acid 3 letter 1 letter Polarity Amino Acid 3
Alanine Ala A nonpolar
Arginine Arg R Polar
Asparagine Asp N Polar
Cysteine Cys C Polar
Glutamic acid Glu E nonpolar
Glycine Gln Q Polar
Histidine His H Polar
Isoleucine Ile I nonpolar
Leucine Leu L Polar
Lysine Lys K nonpolar

46
 Human Genome Project
 How many genes are in the Human Genome?
 Majority of biologists believed a lot more than
 50,000
 Rice genome contains about 50,000 genes
 Big surprise, humans only have about half that many!
 Major changes in the science of molecular biology
 The number of genes in the genome was just the first of many
surprises!
 The belief that the sole function of RNA is to carry the "code" of
instructions for a protein is not true
 It is now known that more than half of the RNA molecules that have
been discovered are "non-coding"
 Systems Biology
 The two surprising discoveries are related
 It is not how many genes; it is how they are regulated that is
important!
 Now everyone is trying to understand regulatory networks - Systems
Biology
14.2A new field has recently emerged called
bioinformatics – application of IT and CS to
molecular biology.
DNA sequencing -
 Constructing phylogenetic (evolutionary) trees
 Competition graphs.
 Modeling ecological landscapes
 Understanding the dynamics of disease
propagation
 Investigating the roles of genes and
proteins,
 Etc.
 The study of RNA molecules is at the forefront of the field of
Systems Biology

47
 The building blocks of a DNA molecule are nucleotide bases—
Adenine, Guanine, Cytosine, and Thymine (AGCT.) arranged in a
sequence.
 Length of sequence is order of 3x10**9
 A special technology allows us to “see” fragments of length 500-
1200 nucleotides.
 How do we piece them together to find out the whole sequence of
the DNA?
 Difference in DNA between 2 people is less than 0.1%.
 Construct a directed graph where V- fragments E – (vi,vj) if end of
fragment i coincides with beginning of fragment j.
 Find an Euler trail Is it unique?
 Are all possible fragments there?
 Possible errors?
Biology – phylogenetic tree

48
Example: studies on bird population
 Ecological landscapes can be modeled using graphs.
 V- represents habitat patches or roosts for birds,
E - represent movement between the patches.
 Graphs help study the structural organization of a landscape,
importance of certain nodes, degree of connectivity between them.
 These properties affect the spread of disease, the vulnerability to
disturbance of the landscape, and other issues related to conservation
15 Knight’s Tour
 A knight's tour is a sequence of moves of a knight on
a chessboard such that the knight visits every square only
once. If the knight ends on a square that is one knight's move
from the beginning square (so that it could tour the board
again immediately, following the same path), the tour
is closed, otherwise it is open.
 The knight's tour problem is the mathematical problem of
finding a knight's tour. Creating a program to find a knight's
tour is a common problem given to computer
science students.[1]
Variations of the knight's tour problem
involve chessboards of different sizes than the usual 8 × 8, as
well as irregular (non-rectangular) boards.
An open knight's tour of
a chessboard

49
 The knight's tour problem is an instance of the more
general Hamiltonian path problem in graph theory. The
problem of finding a closed knight's tour is similarly an
instance of the Hamiltonian cycle problem. Unlike the general
Hamiltonian path problem, the knight's tour problem can be
solved in linear time.
 There are quite a number of ways to find a knight's tour on a
given board with a computer. Some of these methods
are algorithms while others are heuristics.
1.1.1Brute-force algorithms
A brute-force search for a knight's tour is impractical on all but
the smallest boards;[10]
for example, on an 8 × 8 board there
are approximately 4×1051
possible move sequences,[11]
and it is
well beyond the capacity of modern computers (or networks of
computers) to perform operations on such a large set.
However, the size of this number gives a misleading
impression of the difficulty of the problem, which can be
solved "by using human insight and ingenuity ... without much
difficulty."[10]
1.1.2Divide and conquer algorithms
By dividing the board into smaller pieces, constructing tours
on each piece, and patching the pieces together, one can
construct tours on most rectangular boards in linear time - that
is, in a time proportional to the number of squares on the
board. [5][12]
1.1.3Neural network solutions
Closed knight's tour on a 24 × 24 board solved by a neural
network.

50
The Knight's Tour problem also lends itself to being solved by
a neural network implementation.[13]
The network is set up
such that every legal knight's move is represented by a neuron,
and each neuron is initialized randomly to be either "active" or
"inactive" (output of 1 or 0), with 1 implying that the neuron is
part of the final solution. Each neuron also has a state function
(described below) which is initialized to 0.
When the network is allowed to run, each neuron can change
its state and output based on the states and outputs of its
neighbors (those exactly one knight's move away) according to
the following transition rules:
where t represents discrete intervals of time, U(Ni,j) is the state
of the neuron connecting square i to square j, V(Ni,j) is the
output of the neuron from i to square j and G(Ni,j) is the set of
neighbours of the neuron.
 Although divergent cases are possible, the network should
eventually converge, which occurs when no neuron changes its
state from time t to t+1. When the network converges, either
the network encodes a knight's tour or a series of two or more
independent circuits within the same board.

51
16 Common Problems
16.1 House of Santa Claus:
The house in the picture can be represented as a graph, where
each vertex is a node. Try to draw a the house by yourself
fallowing this rules:
• You have to draw a house in one line.
• You must not lift your pencil while
drawing.
• You must not repeat a line.
16.2 Three utilities problem:
The classical mathematical puzzle known as the three utilities
problem; the three cottages problem or sometimes water, gas
and electricity can be stated as follows:
There are three utilities (three vertices) and three houses (three vertices),
and the purpose of this problem is to draw a line from each house to each
facility without the lines ever crossing.
Suppose there are three cottages on a plane (or sphere) and each needs to
be connected to the gas, water, and electricity companies. Without using a
third dimension or sending any of the connections through another
company or cottage, is there a way to make all nine connections without
any of the lines crossing each other?

52
The problem is an abstract mathematical puzzle which imposes constraints
that would not exist in a practical engineering situation. It is part of
the mathematical field of topological graph theory which studies
the embedding of graphs on surfaces. In more formal graph-
theoretic terms, the problem asks whether the complete bipartite
graph K3,3 is planar.[1]
This graph is often referred to as the utility graph in
reference to the problem;[2]
it has also been called the Thomsen graph
16.3 Seven Bridges of Königsberg:
The Seven Bridges of Königsberg is a historically notable problem in
mathematics. Its negative resolution by Leonhard Euler in 1736[16]
laid
the foundations of graph theory and prefigured the idea of topology.[16]
The city of Königsberg in Prussia (now Kaliningrad, Russia) was set
on both sides of the Pregel River, and included two large islands which
were connected to each other, or to the two mainland portions of the
city, by seven bridges. The problem was to devise a walk through the
city that would cross each of those bridges once and only once.
By way of specifying the logical task unambiguously, solutions
involving either
1. reaching an island or mainland bank other than via one of the
bridges, or
2. accessing any bridge without crossing to its other end
are explicitly unacceptable.
Euler proved that the problem has no solution. The difficulty he faced was
the development of a suitable technique of analysis, and of subsequent
tests that established this assertion with mathematical rigor.

53
16.4 Shortest path problem:
In graph theory, the shortest path problem is the problem of finding
a path between two vertices (or nodes) in a graph such that the sum of
the weights of its constituent edges is minimized.
The problem of finding the shortest path between two intersections on a
road map (the graph's vertices correspond to intersections and the edges
correspond to road segments, each weighted by the length of its road
segment) may be modeled by a special case of the shortest path problem in
graphs
Shortest path (A, C, E, D, F) between vertices A and F in the
weighted directed graph
16.5Travelling Salesman Problem (TSP):
The travelling salesman problem (TSP) asks the
following question: "Given a list of cities and the distances
between each pair of cities, what is the shortest possible route
that visits each city and returns to the origin city?" It is an NP-
hard problem in combinatorial optimization, important
in operations research and theoretical computer science.
The travelling purchaser problem and the vehicle routing
problem are both generalizations of TSP.
In the theory of computational complexity, the decision
version of the TSP (where, given a length L, the task is to decide
whether the graph has any tour shorter than L) belongs to the
class of NP-complete problems. Thus, it is possible that
the worst-case running time for any algorithm for the TSP
increases superpolynomially (but no more than exponentially)
with the number of cities.

54
The problem was first formulated in 1930 and is one of the
most intensively studied problems in optimization. It is used as
a benchmark for many optimization methods. Even though the
problem is computationally difficult, a large number of heuristics
and exact algorithms are known, so that some instances with tens
of thousands of cities can be solved completely and even
problems with millions of cities can be approximated within a
small fraction of 1%.
The TSP has several applications even in its purest
formulation, such as planning, logistics, and the manufacture
of microchips. Slightly modified, it appears as a sub-problem in
many areas, such as DNA sequencing. In these applications, the
concept city represents, for example, customers, soldering points,
or DNA fragments, and the concept distance represents travelling
times or cost, or a similarity measure between DNA fragments.
The TSP also appears in astronomy, as astronomers observing
many sources will want to minimize the time spent moving the
telescope between the sources. In many applications, additional
constraints such as limited resources or time windows may be
imposed

55
Bibliography
[4A]:-Harary & Sumner (1980).
[4B]:- Simion (1991).
[4C]:- Kim & Pearl (1983).
[6A]:- C. A. Coulson, B. O'Leary, and R. B. Mallion, Huckel theory for
organic chemists. Academic Press, London, 1978.
[6B]:- Rosen, Kenneth H. (1999), Handbook of Discrete and
Combinatorial Mathematics, Discrete Mathematics and Its Applications,
CRC Press, p. 515
[5]:- Cull, P.; De Curtins, J. (1978)
[10]:- Simon, Dan (2013), Evolutionary Optimization Algorithms, John
Wiley & Sons, pp. 449– 450, ISBN 9781118659502, The knight's tour
problem is a classic combinatorial optimization problem
[11]:-"Enumerating the Knight's Tour
[12]:- arberry, Ian (1997). "An Efficient Algorithm for the Knight's Tour
Problem" . Discrete Applied Mathematics
[13]:- Y. Takefuji, K. C. Lee. "Neural network computing for knight's tour
problems." Neurocomputing, 4(5):249–254, 1992.
[16]:- Euler, Leonhard (1736). "Solutio problematis ad geometriam situs
pertinentis". Comment. Acad. Sci. U. Petrop 8. Shields, Rob (December
2012). "Cultural Topology: The Seven Bridges of Königsburg 1736

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