'Adjacency list' diaporamas de présentation

CS 332: Algorithms

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Graph

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GRAPHS

GRAPHS. CSE, POSTECH. Chapter 16 covers the following topics. Graph terminology : vertex, edge, adjacent, incident, degree, cycle, path, connected component, spanning tree Types of graphs : undirected, directed, weighted

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How many faces do you get from walking the faces of this rotation system of K 4 ?

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Graph Algorithms

Graph Algorithms. Data-Intensive Information Processing Applications ? Session #5. Jimmy Lin University of Maryland Tuesday, March 2, 2010.

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CS 332: Algorithms

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Algorithm Design and Analysis

L ECTURE 7 Greedy Graph Algorithms Topological sort Shortest paths. Algorithm Design and Analysis. CSE 565. Adam Smith. The (Algorithm) Design Process. Work out the answer for some examples Look for a general principle Does it work on *all* your examples? Write pseudocode

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Managing Hierarchical Data in MySQL

Managing Hierarchical Data in MySQL. Mike Hillyer - MySQL AB PHP Quebec 2005. About Me. Mike Hillyer, BSc. Member of the MySQL AB Documentation Team MySQL Core and Pro Certified Top MySQL Expert at www.experts-exchange.com Resident MySQL Expert at SearchDatabase.com

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Depth-first search

COMP171 Fall 2005. Depth-first search . Depth-First Search (DFS). DFS is another popular graph search strategy Idea is similar to pre-order traversal (visit children first) DFS can provide certain information about the graph that BFS cannot

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Minimum Spanning Trees

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Basic Graph Algorithms

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Lecture 19: CONNECTIVITY Sections 8.1 - 8.3

Lecture 19: CONNECTIVITY Sections 8.1 - 8.3. CS1050: Understanding and Constructing Proofs . Spring 2006. Jarek Rossignac. Lecture Objectives. Learn graph terminology. What are the types of graph?. Graph G(V,E) V = set of vertices (non-empty)

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Graphs

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Chapter 14 excerpts

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Spatial and Spatio-temporal Networks Reem Ali, Amr Magdy

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Spatial and Spatio-temporal Networks Reem Ali, Amr Magdy

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Example: Taxonomy of Organisms

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Representing Graphs

Representing Graphs. Adjacency Matrix. Suppose we have a graph G with n nodes. The adjacency matrix is the n x n matrix A=[a ij ] with:. a ij = 1 if (i,j) is an edge a ij = 0 if (i,j) is not an edge. Good for dense graphs!. 0 1 1 1. 1 0 1 1. A =. 1 1 0 1. 1 1 1 0. Example.

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