+ GRAPH Algorithm 2 Dikompilasi dari banyak sumber.

Slides:



Advertisements
Similar presentations
CSE 211 Discrete Mathematics
Advertisements

Discrete Mathematics University of Jazeera College of Information Technology & Design Khulood Ghazal Connectivity Lecture _13.
Chapter 8 Topics in Graph Theory
Lecture 5 Graph Theory. Graphs Graphs are the most useful model with computer science such as logical design, formal languages, communication network,
Walks, Paths and Circuits Walks, Paths and Circuits Sanjay Jain, Lecturer, School of Computing.
13 May 2009Instructor: Tasneem Darwish1 University of Palestine Faculty of Applied Engineering and Urban Planning Software Engineering Department Introduction.
1 Slides based on those of Kenneth H. Rosen Slides by Sylvia Sorkin, Community College of Baltimore County - Essex Campus Graphs.
Introduction This chapter explores graphs and their applications in computer science This chapter explores graphs and their applications in computer science.
 Graph Graph  Types of Graphs Types of Graphs  Data Structures to Store Graphs Data Structures to Store Graphs  Graph Definitions Graph Definitions.
C++ Programming: Program Design Including Data Structures, Third Edition Chapter 21: Graphs.
A Presentation By: Jillian Minuto Troy Norman Alan Leggett Group Advisor: Prof. G. Warrington Graph Theory.
Lists A list is a finite, ordered sequence of data items. Two Implementations –Arrays –Linked Lists.
1 Graph Introduction Definitions. 2 Definitions I zDirected Graph (or Di-Graph) is an ordered pair G=(V,E) such that yV is a finite, non-empty set (of.
CTIS 154 Discrete Mathematics II1 8.2 Paths and Cycles Kadir A. Peker.
1 Section 8.4 Connectivity. 2 Paths In an undirected graph, a path of length n from u to v, where n is a positive integer, is a sequence of edges e 1,
4/17/2017 Section 8.5 Euler & Hamilton Paths ch8.5.
Graphs. Graph A “graph” is a collection of “nodes” that are connected to each other Graph Theory: This novel way of solving problems was invented by a.
MCA 520: Graph Theory Instructor Neelima Gupta
KNURE, Software department, Ph , N.V. Bilous Faculty of computer sciences Software department, KNURE Discrete.
Discrete Mathematics Lecture 9 Alexander Bukharovich New York University.
GRAPH Learning Outcomes Students should be able to:
Graphs CS /02/05 Graphs Slide 2 Copyright 2005, by the authors of these slides, and Ateneo de Manila University. All rights reserved Definition.
Graph Theoretic Concepts. What is a graph? A set of vertices (or nodes) linked by edges Mathematically, we often write G = (V,E)  V: set of vertices,
(CSC 102) Lecture 29 Discrete Structures. Graphs.
CSE, IIT KGP Graph Theory: Introduction Pallab Dasgupta Dept. of CSE, IIT
CSNB143 – Discrete Structure Topic 9 – Graph. Learning Outcomes Student should be able to identify graphs and its components. Students should know how.
Based on slides by Y. Peng University of Maryland
Indian Institute of Technology Kharagpur PALLAB DASGUPTA Graph Theory: Introduction Pallab Dasgupta, Professor, Dept. of Computer Sc. and Engineering,
Graphs.  Definition A simple graph G= (V, E) consists of vertices, V, a nonempty set of vertices, and E, a set of unordered pairs of distinct elements.
Discrete Structures Trees (Ch. 11)
Fall 2015 COMP 2300 Discrete Structures for Computation Donghyun (David) Kim Department of Mathematics and Physics North Carolina Central University 1.
Basic Notions on Graphs. The House-and-Utilities Problem.
Chapter 1 Fundamental Concepts Introduction to Graph Theory Douglas B. West July 11, 2002.
Chapter 5 Graphs  the puzzle of the seven bridge in the Königsberg,  on the Pregel.
1 Graphs Theory UNIT IV. 2Contents  Basic terminology,  Multi graphs and weighted graphs  Paths and circuits  Shortest path in weighted graph  Hamiltonian.
Introduction to Graph Theory
Topics Paths and Circuits (11.2) A B C D E F G.
An Introduction to Graph Theory
Graph Theory and Applications
Lecture 10: Graph-Path-Circuit
CSCI 115 Chapter 8 Topics in Graph Theory. CSCI 115 §8.1 Graphs.
Graphs 9.1 Graphs and Graph Models أ. زينب آل كاظم 1.
Graph theory and networks. Basic definitions  A graph consists of points called vertices (or nodes) and lines called edges (or arcs). Each edge joins.
 Quotient graph  Definition 13: Suppose G(V,E) is a graph and R is a equivalence relation on the set V. We construct the quotient graph G R in the follow.
Introduction to Graph Theory
+ GRAPH Algorithm Dikompilasi dari banyak sumber.
Graph Theory Unit: 4.
CSE, IIT KGP Graph Theory: Introduction Pallab Dasgupta Dept. of CSE, IIT
+ GRAPH Algorithm Dikompilasi dari banyak sumber.
Chapter 20: Graphs. Objectives In this chapter, you will: – Learn about graphs – Become familiar with the basic terminology of graph theory – Discover.
Graphs Definition: a graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected.
Week 11 - Wednesday.  What did we talk about last time?  Graphs  Paths and circuits.
(CSC 102) Lecture 30 Discrete Structures. Graphs.
1 GRAPH Learning Outcomes Students should be able to: Explain basic terminology of a graph Identify Euler and Hamiltonian cycle Represent graphs using.
1 Lecture 5 (part 2) Graphs II (a) Circuits; (b) Representation Reading: Epp Chp 11.2, 11.3
رياضيات متقطعة لعلوم الحاسب MATH 226. Chapter 10.
Trees.
An Introduction to Graph Theory
Graphs: Definitions and Basic Properties
Special Graphs By: Sandeep Tuli Astt. Prof. CSE.
Grade 11 AP Mathematics Graph Theory
Based on slides by Y. Peng University of Maryland
Can you draw this picture without lifting up your pen/pencil?
Walks, Paths, and Circuits
Graph Theory What is a graph?.
5/9/2019 Discrete Math II Howon Kim
Discrete Math II Howon Kim
Discrete Math II Howon Kim
Based on slides by Y. Peng University of Maryland
Concepts of Computation
Presentation transcript:

+ GRAPH Algorithm 2 Dikompilasi dari banyak sumber

+ Walk … A walk in the graph G = (V, E) is a finite sequence of the form v i0,e j1,v i1,e j2,...,e jk,v ik, The walk starts at a vertex v i0 is the initial vertex and v ik is the terminal vertex. k is the length of the walk. A zero length walk is just a single vertex v i0 It is allowed to visit a vertex or go through an edge more than once. A walk is open if v i0 <> v ik. Otherwise it is closed.

+ Example of walk The walk v 2, e 7, v 5, e 8, v 1, e 8, v 5, e 6, v 4, e 5, v 4, e 5, v 4 is open. On the other hand, the walk v 4, e 5, v 4, e 3, v 3, e 2, v 2, e 7, v 5, e 6, v 4 Is closed.

Trail A walk is a trail if any edge is traversed at most once. Then, the number of times that the vertex pair u, v can appear as consecutive vertices in a trail is at most the number of parallel edges connecting u and v.

+ Example of trail The walk in the graph v 1, e 8, v 5, e 9, v 1, e 1, v 2, e 7, v 5, e 6, v 4, e 5, v 4, e 4, v 4 Is trail.

A trail is a path if any vertex is visited at most once except possibly the initial and terminal vertices when they are the same. A closed path is a circuit. Path and Circuit

Example of path and circuit  The walk in the graph v 2, e 7, v 5, e 6, v 4, e 3, v 3  is a path. While the walk below v 2, e 7, v 5, e 6, v 4, e 3, v 3, e 2, v 2  is a circuit

+ Graph Operation The complement of the simple graph G = (V, E) is the simple graph G = (V, E), where the edges in E are exactly the edges not in G.

+ Binary operation between graphs If the graphs G=(V,E) and G’ =(V’,E’)are simple and V’ ⊆ V, then the difference graph is G − G ′ = (V, E’’), where E’’ contains those edges from G that are not in G’ (simple graph).

+ Binary operation between graphs (2) Here are some binary operations between two simple graphs G1 = (V1,E1) and G2 = (V2,E2): The union is G1 ∪ G2 =(V1 ∪ V2, E1 ∪ E2)(simple graph). The intersection is G1 ∩ G2 = (V1 ∩ V2, E1 ∩ E2) (simple graph). The ring sum G1 ⊕ G2 is the subgraph of G1 ∪ G2 induced by the edge set E1 ⊕ E2 (simple graph). Note! The set operation ⊕ is the symmetric difference, i.e. E1 ⊕ E2 =(E1 − E2) ∪ (E2 − E1). Since the ring sum is a subgraph induced by an edge set, there are no isolated vertices. All three operations are commutative and associative.

+ Example From G1 and G2, define G1 ∪ G2, G1 ∩ G2 and G1 ⊕ G2

+ Graph induced by removal vertex If v is a vertex of the graph G = (V, E), then G − v is the subgraph of G induced by the vertex set V − {v}. We call this operation the removal of a vertex.

+ Graph induced by removal edge Similarly, if e is an edge of the graph G = (V,E), then G − e is graph (V,E ′ ), where E’ is obtained by removing e from E. This operation is known as removal of an edge.

+ References Graph Theory handbook by Keijo Ruohonen Slide of Graph mining seminar by Prof. Ehud Gudes. 2013