The document discusses various topics related to graphs:
- It defines directed and undirected graphs, paths, connected graphs, trees, degree, isomorphic graphs, cut sets, and labeled graphs.
- Key aspects include paths being sequences of vertices with edges connecting them, connected graphs having paths between all vertex pairs, trees being connected and acyclic graphs, and isomorphic graphs having the same structure.
- It also covers graph concepts such as degrees measuring incident edges, cut sets separating graphs, and labeling providing additional data to graphs' vertices or edges.
In computer science, tree traversal (also known as tree search) is a form of graph traversal and refers to the process of visiting (checking and/or updating) each node in a tree data structure, exactly once. Such traversals are classified by the order in which the nodes are visited. The following algorithms are described for a binary tree, but they may be generalized to other trees as well.
OVERVIEW:
Introduction
Definition
Example of Threaded BT.
Types & Structure
One-way .
Double-way.
Structure.
Traversal
Algorithm for Traversal
Traversal Example
Inserting
Algorithm for Inserting
Inserting Example
Comparison With Binary Tree
Advantages and Disadvantages
Why Threaded BT are used?
Conclusion
Reference
Traversal is a process to visit all the nodes of a tree and may print their values too. Because, all nodes are connected via edges (links) we always start from the root (head) node. That is, we cannot randomly access a node in a tree.
It is related to Analysis and Design Of Algorithms Subject.Basically it describe basic of topological sorting, it's algorithm and step by step process to solve the example of topological sort.
This presentation is useful to study about data structure and topic is Binary Tree Traversal. This is also useful to make a presentation about Binary Tree Traversal.
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
In computer science, tree traversal (also known as tree search) is a form of graph traversal and refers to the process of visiting (checking and/or updating) each node in a tree data structure, exactly once. Such traversals are classified by the order in which the nodes are visited. The following algorithms are described for a binary tree, but they may be generalized to other trees as well.
OVERVIEW:
Introduction
Definition
Example of Threaded BT.
Types & Structure
One-way .
Double-way.
Structure.
Traversal
Algorithm for Traversal
Traversal Example
Inserting
Algorithm for Inserting
Inserting Example
Comparison With Binary Tree
Advantages and Disadvantages
Why Threaded BT are used?
Conclusion
Reference
Traversal is a process to visit all the nodes of a tree and may print their values too. Because, all nodes are connected via edges (links) we always start from the root (head) node. That is, we cannot randomly access a node in a tree.
It is related to Analysis and Design Of Algorithms Subject.Basically it describe basic of topological sorting, it's algorithm and step by step process to solve the example of topological sort.
This presentation is useful to study about data structure and topic is Binary Tree Traversal. This is also useful to make a presentation about Binary Tree Traversal.
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
Trees. Defining, Creating and Traversing Trees. Traversing the File System
Binary Search Trees. Balanced Trees
Graphs and Graphs Traversal Algorithms
Exercises: Working with Trees and Graphs
this presentation is made for the students who finds data structures a complex subject
this will help students to grab the various topics of data structures with simple presentation techniques
best regards
BCA group
(pooja,shaifali,richa,trishla,rani,pallavi,shivani)
Analysis and design of algorithms part 3Deepak John
Graphs and graph traversals. Strongly connected components of a Directed graph. Biconnected components of an undirected graph.
Transitive closure of a Binary relation. Warshalls algorithm for Transitive closure. All pair shortest path in graphs. Dynamic programming. Constructing optimal binary search trees.
Graph in data structure it gives you the information of the graph application. How to represent the Graph and also Graph Travesal is also there many terms are there related to garph
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2. Topics in discussion
Introduction to graphs
Directed and undirected graphs
Paths
Connected graphs
Trees
Degree
Isomorphic graphs
Cut set
Labeled graphs
Hamiltonian circuit
3. Introduction to graphs
• Graph is a mathematical structure used to model pair wise
relations between objects from a certain collection.
Vertices
Edges
4. Directed and undirected graphs
• A graph is said as directed graph whose definition makes
reference to edges which are directed. Ie,
edges which are ordered pair of vertices.
• A graph is said as undirected graph whose definition makes
reference to unordered pairs of vertices as edges is known as
an undirected graph.
5. Paths
• A path in a graph is a sequence of vertices such that from
each of its vertices there is an edge to the next vertex in the
sequence.
• The length of a path is the number of edges on it. The
length can be zero for the case of a single vertex.
6. • A path may be infinite.
• A finite path always has a first vertex, called its start vertex,
and a last vertex, called its end vertex.
• Both of them are called terminal vertices
of the path.
• The other vertices in the path are internal vertices.
B
A
E Path= A B D C E
D
C A- Start vertex
E- End vertex
7. Simple path
• A graph with no loops or multiple edges is called a simple graph.
• A path with no repeated vertices is called a simple path.
• The path from v1 to v4 is said to be simple path as
vertices is touched more than once.
• The path from v1 to v4 is not simple as
v1 is touched twice or looped.
cycle:
• Simple path, except that the last vertex is the same as
the first vertex. Its also known as a circuit or circular path.
A B
E
C Path= A E C A
D
8. Connected graph
• Two vertices vi, vj in a graph G is said to be connected only if
there is a path in G between vi and vj.
• A undirected graph is said to be connected graph if every pair
of distinct vertices vi, vj are connected.
Connected undirected graph
9. • In the case of an undirected graph, which is not connected,the
maximal connected subgraph is called as a connected
component or simply a component.
The graph below has 3 connected components.
10. Connected directed graph
• A directed graph is said to be strongly connected only if every
pair of distinct vertices vi, vj are connected.
• If there is a directed path from vi to vj then there must be a
directed path from vj to vi.
A strongly connected directed graph
11. • Strongly connected components of directed graph
• The below graph is not strongly connected but is said to
possess two strongly connected components.
12. Trees
• A tree is defined to be a connected acyclic graph. The
following properties are satisfied by a tree:
There exist a path between any two vetices of the tree
No cycles must be present in the tree ie, trees are
acyclic.
A Tree Not a tree
13. • Terms like parent, child, ancestors,level are missing but both
the definitions of tree in datastructures and in graph share
the same properties of connectedness and acyclicity.
Both have the same properties of connectedness and
acyclicity.
14. Degree
• The degree of vertex in an undirected graph is the number of
edges incident to that vertex.
• A vertex with degree one is called pendent vertex or end
vertex.
• A vertex with degree zero and hence has no incident edges is
called an isolated vertex.
A V1
B
Isolated vertex
Pendent vertex
In the undirected graph vertex v3 has the degree 3
And vertex v2 has the degree 2
15. Degree in directed graph
• Degree of directed graph has two types
i. Indegree
No of edges with their head towards the vertex.
ii. Outdegree
No of edges with their tail towards the vertex.
Indegree of vertex v2 is 2
and
Outdegree of vertex v1 is 1.
17. Isomorphic graphs
• Isomorphism
– Two graphs are isomorphic, if they are structurally
identical, Which means that they correspond in all
structural details.
– Formal vertex-to-vertex and edge –to-edge
correspondence is called isomorphism.
• Two graph are said to be isomorphic if
They have the same no of vertices.
They have the same number of edges.
They have an equal number of vertices with a given
degree.
18. Verifying Isomorphic graph
Graph B
Graph A
Vertices(A) : a b c d e
Vertices(B): q p r s t
Degree of 2 3 3 3 1
vertices:
Edges(A): e1 e2 e3 e4 e5 e6
Edges(B): e’1 e’4 e’3 e’2 e’5 e’6
19. Examples for non isomorphic graphs :
i)
u2 v2
u3
u1 v1 v3
u5 u4 v4
1st graph has more edges than 2nd.
20. ii) 2nd graph has vertex of degree 1, 1st graph
doesn't.
u2 v2
u3 v3
u1 v1
u5 u4 v5 v4
22. Cut set
• Cut set is a connected graph G is the set of edges whose
removal from G leaves G disconnected, Provided the removal
of no proper sebset of these edges disconnects the graph G.
• Cut set are also called proper cut set or minimal cut set.
23. • If one can remove a vertex (and all incident edges) and
produce a graph with more components, the vertex is called a
cut vertex or articulation point.
• Similarly if removal of an edge creates more components the
edge is called a cut edge or bridge.
• The cut-set of the cut is the set of edges whose end points are
in different subsets of the partition.
Edges are said to be crossing the cut if they are in its cut-set.
24. Labeled graph
• A graph G is called a labeled graph if its edges and/or vertices
are assigned some data.
• A graph labeling is the assignment of labels, traditionally
represented by integers, to the edges or vertices, or both, of a
graph.
• If the edge e is assigned a non-negative number then it is
called the weight or length of the edge e.
25. • Vertex-labeled graph
• If all the vertices in a graph are given a label then it is
vertex-labeled graph
• Edge-labeled graph
• If all the Edges in a graph are given a label then it is
Edge-labeled graph
26. Hamiltonian circuit
• Hamiltonian paths and circuits are named after the
mathematician ,William Rowan Hamilton.
• A Hamiltonian circuit in a connected graph is defined as a
closed walk that traverses every vertex of G exactly once.
also called Hamiltonian cycles.
• It is called as circuit if it includes every vertex of G. If any edge
is removed then it is Hamiltonian path.
Hamiltonian circuit:
{v1,v3,v4,v2,v6,v5,v1}
The above is a Hamiltonian circuit
as each and every vertex is traversed once
And completes the circuit by ending in starting point.
27. • A Hamiltonian path or traceable path is a path that visits each
vertex exactly once. Its also called as a traceable graph.
a b
Hamiltonian path
d c
• A graph is Hamiltonian-connected if for every pair of vertices
there is a Hamiltonian path between the two vertices.