'Undirected graphs' presentation slideshows

2620001 Data Structures

2620001 Data Structures. Graph Data Structure. What is a graph?. A data structure that consists of a set of nodes ( vertices ) and a set of edges that relate the nodes to each other The set of edges describes relationships among the vertices. Formal definition of graphs.

1.22k views • 110 slides

Introduction to Algorithms Spanning Trees

Introduction to Algorithms Spanning Trees. CSE 680 Prof. Roger Crawfis. Tree. We call an undirected graph a tree if the graph is connected and contains no cycles . Trees: Not Trees:. Not connected. Has a cycle. Number of Vertices.

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Random Walk on Graphs and its Algorithmic Applications

Random Walk on Graphs and its Algorithmic Applications. Shengyu Zhang Winter School, ITCSC@CUHK, 2009. Random walk on graphs. On an undirected graph G: Starting from vertex v 0 Repeat for a number of steps: Go to a random neighbor. Simple but powerful. Road map: Random walk. Parameters

1.21k views • 67 slides

Algorithmic and Economic Aspects of Networks

Algorithmic and Economic Aspects of Networks. Nicole Immorlica. Learning in Networks. Last lecture : - Actions chosen probabilistically - Payoffs action-dependent and unknown Trick was to learn to play a high-payoff action. Game Theory in Networks. This lecture :

807 views • 67 slides

Introduction to biological networks

Introduction to biological networks. GENOME. protein-gene interactions. PROTEOME. protein-protein interactions. METABOLISM. Bio-chemical reactions. Citrate Cycle. Types of biological network. Genetic regulatory network Protein-protein interaction network Metabolic network

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Representation and Learning in Directed Mixed Graph Models

Representation and Learning in Directed Mixed Graph Models. Ricardo Silva Statistical Science/CSML, University College London ricardo@stats.ucl.ac.uk Networks: Processes and Causality, Menorca 2012. Graphical Models. Graphs provide a language for describing independence constraints

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CS 388: Natural Language Processing: Discriminative Training and Conditional Random Fields (CRFs) for Sequence Labeling

CS 388: Natural Language Processing: Discriminative Training and Conditional Random Fields (CRFs) for Sequence Labeling. Raymond J. Mooney University of Texas at Austin. 1. Joint Distribution.

627 views • 37 slides

Graph Traversals

Graph Traversals. Introduction Breadth-First Traversal. The Algorithm. Example. Implementation. Depth-First Traversals. Algorithms. Example. Implementation. Some traversal applications: Connected Components Strongly Connected Components Edge Classification Review Questions.

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All-Distances Sketches, Revisited: HIP Estimators for Massive Graphs Analysis

All-Distances Sketches, Revisited: HIP Estimators for Massive Graphs Analysis. Edith Cohen Microsoft Research . Presented by: Thomas Pajor Microsoft Research . Very Large Graphs. Model many types of relations and interactions Call detail data, email exchanges Web crawls

532 views • 26 slides

Graphs

Graphs. Eric Roberts CS 106B February 25, 2013. 1. 3. 4. 5. 2. Examples of graphs. Graph terminology. Designing the graph.h interface. Traversal strategies: depth-first and breadth-first search. Defining a graph in terms of sets. Outline. Examples of Graphs. Examples of Graphs.

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Chapter 27 Graph Applications

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Creation of Automaton Classes from Graphical Models and Automatic Solution for Inverse Problem

Creation of Automaton Classes from Graphical Models and Automatic Solution for Inverse Problem. Yuri A. Gubin student of SPb SU ITMO supervised by Anatoly A. Shalyto Ph. D, prof. CTD, SPb SU ITMO. Outline. Automata- b ased p rogramming Reverse engineering DOT modeling language

310 views • 24 slides

Force Directed Layouts

Force Directed Layouts. Hank Childs, University of Oregon. November 22 nd , 2013. Upcoming Schedule. 11/21: alternate lecture #1 (force directed layouts) 5pm , Deschutes 220 11 / 22 : uncertainty visualization (KP ) 12/2: alternate lecture #2 (force directed layouts) 2pm , HEDCO 142

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Design and Analysis of Algorithms Minimum Spanning trees

Design and Analysis of Algorithms Minimum Spanning trees. Haidong Xue Summer 2012, at GSU. What are trees in undirected graphs?. We are familiar with a rooted directed tree, such as: Binary search tree Heap They look like: You know which one the root is Who the parent is

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Graphs

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A Recursive Greedy Algorithm for Walks in Directed Graphs

Discussion based on :-. A Recursive Greedy Algorithm for Walks in Directed Graphs. Conclusions !!. Polynomial time algorithms for SOP and SOP-TW that have a poly-logarithmic approximation ratio .

278 views • 19 slides

CMSC 341

CMSC 341. Graphs. Basic Graph Definitions. A graph G = (V,E) consists of a finite set of vertices , V, and a set of edges , E. Each edge is a pair (v,w) where v, w ? V. V and E are sets, so each vertex v ? V is unique, and each edge e ? E is unique.

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Lecture 28: Graph ADT

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Postman Problems Assume and is connected.

5.4 T-joins and Postman Problems. Postman Problems Assume and is connected. Thm 5.23: A connected graph has an Euler tour if and only if every node of has even degree.

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Summary 3

Summary 3. Discrete Probability (sections 5.1 - 5.2) Experiments, outcomes, and sample space Use counting techniques to determine sample space p(s) for each s?S 0 ? p(s) ? 1 for each s?S ? s?S p(s) = 1 If all outcome are equally probable, then p(s) = 1/|S|

241 views • 9 slides