'Adjacency matrix' presentation slideshows

Data Structures and Algorithms - 05 Graph Algorithms

Data Structures and Algorithms - 05 Graph Algorithms. Fall 2004 Major part of this presentation is courtesy of Dr.Bingol. Content. Motivation Graph Directed Graph Undirected Graph Representation Implementation Algorithms Graph Traversal Shortest Path Minimum Cost Spanning Trees

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Connected Components & All Pairs Shortest Paths

Connected Components & All Pairs Shortest Paths. Presented by Wooyoung Kim 3/4/09 CSc 8530 Parallel Algorithms , Spring 2009 Dr. Sushil K. Prasad. Outline. Adjacent matrix and connectivity matrix Parallel algorithm for computing connectivity matrix

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Graph Algorithms

Graph Algorithms. Andreas Klappenecker [based on slides by Prof. Welch]. Directed Graphs. Let V be a finite set and E a binary relation on V, that is, E ?VxV. Then the pair G=(V,E) is called a directed graph . The elements in V are called vertices . The elements in E are called edges .

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Graphs

Graphs. Chapter 12 Graphs Overview Graphs provide a rich and powerful way to model relations. Chapter Objectives. 1. To review basic graph vocabulary and concepts. 2. To develop classes that model various kinds of graphs.

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Elementary Graph Algorithms

Elementary Graph Algorithms. PART-1. Graph Terminology. A graph G = ( V , E ) V = set of vertices E = set of edges In an undirected graph: edge ( u , v ) = edge ( v , u ) In a directed graph: edge ( u , v ) goes from vertex u to vertex v , notated u ? v

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Chapter 16

Chapter 16. Graphs. Outline. Graph Categories Digraph Connectedness of Digraph Adjacency Matrix, Set vertexInfo Object Breadth-First Search Algorithm Depth first Search Algorithm. Strong Components Graph G and Its Transpose G T Shortest-Path Dijkstra Minimum-Path Algorithm

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Graphs

Graphs. Chapter 13. Preview Graphs are an important mathematical concept that have significant applications not only in computer science, but also in many other fields. You can view a graph as a mathematical construct, a data structure, or an abstract data type.

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Chapter 6

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Complex networks and random matrices.

Complex networks and random matrices. Geoff Rodgers School of Information Systems, Computing and Mathematics. Plan. Introduction to scale free graphs Small world networks Static model of scale free graphs Eigenvalue spectrum of scale free graphs Results Conclusions. Scale Free Networks.

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CS 332: Algorithms

CS 332: Algorithms. Graph Algorithms . 7. 10. i = [7,10]; i ? low = 7; i ? high = 10. 5. 11. 17. 19. 4. 8. 15. 18. 21. 23. Interval Trees. The problem: maintain a set of intervals E.g., time intervals for a scheduling program:. 7. 10.

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Jimmy Lin The iSchool University of Maryland Wednesday, February 6, 2008

Cloud Computing Lecture #4 Graph Algorithms with MapReduce. Jimmy Lin The iSchool University of Maryland Wednesday, February 6, 2008.

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Graph Operations And Representation

Graph Operations And Representation. Sample Graph Problems. Path problems. Connectedness problems. Spanning tree problems. 2. 4. 3. 8. 8. 1. 6. 10. 2. 4. 5. 4. 4. 3. 5. 9. 11. 5. 6. 7. 6. 7. Path Finding. Path between 1 and 8 . Path length is 20 . 2. 4. 3. 8.

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Gene Correlation Networks

Gene Correlation Networks. Jin Chen CSE891-001 Fall 2012. Gene expression. Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product It is used by all known life.

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Error-Correction Coding Using Combinatorial Representation Matrices

Joint Mathematics Meetings Washington, DC, January 5-8, 2009 (Monday - Thursday) . Error-Correction Coding Using Combinatorial Representation Matrices. Li Chen, Ph.D. Department of Computer Science and Information Technology University of the District of Columbia 4200 Connecticut Avenue, N.W.

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Graph Theoretical Techniques for Image Segmentation

Graph Theoretical Techniques for Image Segmentation. Region Segmentation. Region Segmentation. Find sets of pixels, such that All pixels in region i satisfy some constraint of similarity. Graph.

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Role Analysis in Social Networks

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Shortest Path Problems

Shortest Path Problems. Directed weighted graph. Path length is sum of weights of edges on path. The vertex at which the path begins is the source vertex. The vertex at which the path ends is the destination vertex. 1. 7. Example. 8. 6. 2. 1. 3. A path from 1 to 7 . 3. 1. 16.

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Representing Graphs

Representing Graphs. Wade Trappe. Lecture Overview. Introduction Some Terminology Paths Adjacency Matrix. Paths and Cycles, pg. 1.

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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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Relevant Subgraph Extraction

Relevant Subgraph Extraction. Based on : P. Dupont , J. Callut , G. Dooms, J.-N. Monette and Y. Deville. Relevant subgraph extraction from random walks in a graph. RR 2006-07, Catholic University of Louvain , November 2006. Longin Jan Latecki. Relevant Subgraph Extraction .

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