'Random graphs' presentation slideshows

341: Introduction to Bioinformatics

938 views • 75 slides

Random Dot Product Graphs

IPAM Intelligent Extraction of Information from Graphs & High Dimensional Data July 26, 2005. Random Dot Product Graphs. Ed Scheinerman Applied Mathematics & Statistics Johns Hopkins University. Coconspirators. Libby Beer John Conroy (IDA) Paul Hand (Columbia) Miro Kraetzl (DSTO)

926 views • 75 slides

Graph Colouring

Graph Colouring. Various Models. an obvious model SAT sets demo random graphs chromatic number maxCol phase transition. kCol. We are given a graph G = (V,E) where V is set of vertices and E is set of edges G is simple, undirected graph.

813 views • 72 slides

LECTURE 3 Complex Network Models Properties of Protein-Protein Interaction Networks

LECTURE 3 Complex Network Models Properties of Protein-Protein Interaction Networks Usage of KNApSack Database. Complex Network Models: Average Path length L, Clustering coefficient C, Degree Distribution P(k) help understand the global structure of the network.

822 views • 56 slides

Quantum Random Walks

Combinatorial and Computational Aspects of Statistical Physics/ Random Graphs and Structures Cambridge, September 5, 2002. Quantum Random Walks. Julia Kempe Computer Science Division and Department of Chemistry, University of California, Berkeley &

822 views • 53 slides

Social Network Analysis

Social Network Analysis. Introduction. What is Network Analysis?. Social network analysis is a method by which one can analyze the connections across individuals or groups or institutions. That is, it allows us to examine how political actors or institutions are interrelated .

1.17k views • 47 slides

The Science of Social Networks

The Science of Social Networks. or, how I almost know a lot of famous people. Kentaro Toyama Microsoft Research India Indian Institute of Science September 19, 2005. Outline. Small Worlds Random Graphs Alpha and Beta Power Laws Searchable Networks Six Degrees of Separation. Outline.

599 views • 46 slides

Protein-Protein Interaction Network

Protein-Protein Interaction Network. Gautam Chaurasia 08.07.04. Overview. Introduction. Three Different Models : Structure of the protein-protein interaction network. Non-power law. Evolutoin of the network. Power Law Random Graphs.

972 views • 46 slides

HY-483 Presentation

421 views • 29 slides

General Database Statistics Using Maximum Entropy

439 views • 28 slides

1 Scale-free networks: mathematical properties

1 Scale-free networks: mathematical properties. Random graphs : classical field in graph theory. Well studied analytically and numerically. Scale-free networks : quite new. Properties were mostly studied numerically and heuristically (sofar). Nice review (suggested reading of today):

428 views • 27 slides

Small World Networks

Scotty Smith February 7, 2007. Small World Networks. Papers. M.E.J.Newman. Models of the Small World: A Review . J.Stat.Phys. Vol. 101, 2000, pp. 819-841. M.E.J. Newman, C.Moore and D.J.Watts. Mean-field solution of the small-world network model. Phys. Rev. Lett. 84, 3201-3204 (2000).

369 views • 22 slides

Randomness Conductors

342 views • 22 slides

Peer-to-Peer and Grid Computing

Exercise Session 3 (TUD Student Use Only) ?. Peer-to-Peer and Grid Computing. Agenda. Graph Metrics Small World Random Graph Power Law. Gaph Metrics. E(N(i)) = number of edges between neighbors of i d(i) = degree of i. Source: Wikipedia. Important Graph Metrics.

356 views • 21 slides

Finding a maximum independent set in a sparse random graph

Finding a maximum independent set in a sparse random graph. Uriel Feige and Eran Ofek. Max Independent Set. Largest set of vertices that induce no edge. NP-hard, even to approximate. NP-hard on planar graphs.

387 views • 20 slides

COMS 6998-06 Network Theory Week 2: September 15, 2010

COMS 6998-06 Network Theory Week 2: September 15, 2010. Dragomir R. Radev Wednesdays, 6:10-8 PM 325 Pupin Terrace Fall 2010. (3) Random graphs. Statistical analysis of networks. We want to be able to describe the behavior of networks under certain assumptions.

335 views • 19 slides

Random-Graph Theory

Random-Graph Theory. The Erdos-Renyi model.

780 views • 16 slides

A Model Using Random Graph Theory

A Model Using Random Graph Theory. PRESENTED TO: CSIIR Workshop Oak Ridge National Lab PRESENTED BY*: Christopher Griffin Penn State Applied Research. * Richard R. Brooks of Clemson University contributed to this study. Goals of Presentation .

235 views • 16 slides

Bridg -it by David Gale

Bridg -it by David Gale. Bridg -it on Graphs. Two players and alternately claim edges from the blue and the red lattice respectively. Edges must not cross. Objective: build a bridge 1: connect left and right 2: connect bottom and top Who wins Bridg -it?. Who wins Bridg -it?.

222 views • 8 slides

A SEQUENTIAL IMPORTANCE SAMPLING ALGORITHM FOR GENERATING RANDOM GRAPHS WITH PRESCRIBED DEGREES

A SEQUENTIAL IMPORTANCE SAMPLING ALGORITHM FOR GENERATING RANDOM GRAPHS WITH PRESCRIBED DEGREES. (Introduction). For example d = (2, 2, 2). Other Algorithms. 1 . Algorithms for random graphs with given degree distributions . 2 . The Pairing Model . 3 . Algorithms Based on the Pairing Model

157 views • 6 slides