'Graph terminology' presentation slideshows

Discrete Mathematics

Discrete Mathematics. Chapter 9 Graphs. ???? ????? ??? (Lingling Huang). Outline. 9.1 Graph and Graph Models 9.2 Graph Terminology and Special Types of Graphs 9.3 Representing Graphs and Graph Isomorphism 9.4 Connectivity 9.5 Euler and Hamiltonian Paths

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Discrete Mathematics

Discrete Mathematics. Chapter 9 Graphs. Outline. 9.1 Graph and Graph Models 9.2 Graph Terminology and Special Types of Graphs 9.3 Representing Graphs and Graph Isomorphism 9.4 Connectivity 9.5 Euler and Hamiltonian Paths 9.6 Shortest-Path Problems 9.7 Planar Graphs

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Combinatorial Mathematics

Combinatorial Mathematics. Chapter 9 Graphs. ???? ????? ???. Outline. 9.1 Graph and Graph Models 9.2 Graph Terminology and Special Types of Graphs 9.3 Representing Graphs and Graph Isomorphism 9.4 Connectivity 9.5 Euler and Hamiltonian Paths 9.6 Shortest-Path Problems

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Outline

Outline. 9.1 Graph and Graph Models 9.2 Graph Terminology and Special Types of Graphs 9.3 Representing Graphs and Graph Isomorphism 9.4 Connectivity 9.5 Euler and Hamiltonian Paths 9.6 Shortest-Path Problems 9.7 Planar Graphs 9.8 Graph Coloring. G =( V , E ) , where

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Chapter 9

Chapter 9. Graphs. Chapter 8. Graphs. Old subject with many modern applications Introduced by L. Euler, who used it to solve the famous Konisberg bridge problem. Example applications: determine if a circuit can be implemented on a planer circuit board (planer graph)

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Graph Algorithms Using Depth First Search

Graph Algorithms Using Depth First Search. Analysis of Algorithms Week 8, Lecture 1. Prepared by John Reif, Ph.D. Distinguished Professor of Computer Science Duke University. Graph Algorithms Using Depth First Search. Graph Definitions DFS of Graphs Biconnected Components

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Algorithms and Data Structures Lecture XI

Algorithms and Data Structures Lecture XI. Simonas Šaltenis Nykredit Center for Database Research Aalborg University simas@cs.auc.dk. This Lecture. Longest Common Subsequence algorithm Graphs – principles Graph representations adjacency list adjacency matrix Traversing graphs

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Algorithms and Data Structures Lecture XI

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Algorithms and Data Structures Lecture XI

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Algorithms and Data Structures Lecture XI

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Chapter 7: Graph

Data Structure and Algorithms. Chapter 7: Graph. 7.1 ??????? 7.2 ?????? 7.2.1 ????? 7.2.2 ??? 7.2.3 ???? 7.2.4 ????? 7.3 ???? 7.4 ????? 7.5 ????????? 7.6 ????. 7.1 Definition of Graphs. Consists of vertices ???) and edge( ?? or arc ( ?? which are pairs of vertices. G=(V,A)

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Graph Terminology

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Graph Terminology

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Algorithms and Data Structures Lecture XI

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Graphs

Graphs. 400. Nantes. Paris. start. 350. 590. 120. fill pan with water. take egg from fridge. Grenoble. 340. Lyon. 320. 420. Monaco. add salt to water. break egg into pan. 510. Bordeaux. Marseille. boil water. An undirected graph. A directed graph. Graph definitions.

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Graph Theory (Networks)

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Graphs

Denver. Chicago. LA. Miami. Tampa. Graphs. A Graph G = (V, E) consists of V, a nonempty set of vertices (or nodes ) and E, a set of edges . Each edge has either one or two vertices associated with it, called its endpoints. An edge is said to connect its endpoints.

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Graph Terminology

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9.2 Graph Terminology and Special Types Graphs

9.2 Graph Terminology and Special Types Graphs. Basic Terminology Definition 1: Two vertices u and v in an undirected graph G are called adjacent (or neighbors ) in G if u and v are endpoints of an edge of G .

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Course : Algorithms in the Real World (296.3) Instructor : Bruce Maggs Teaching Assistant: Harish Chandran

Course : Algorithms in the Real World (296.3) Instructor : Bruce Maggs Teaching Assistant: Harish Chandran Based on a course developed at Carnegie Mellon primarily by Guy Blelloch URL : www.cs.duke.edu/courses/spring11/cps296.3. Expected Background. Asymptotic analysis

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