'Random network' presentation slideshows

Small World Networks

1.12k views • 74 slides

Lecture 2 Complex Network Models Properties of Protein-Protein Interaction Networks Handling Multivariate data: Concept

Lecture 2 Complex Network Models Properties of Protein-Protein Interaction Networks Handling Multivariate data: Concept and types of metrics, distances etc. Hierarchical clustering Self organizing Mapping. Complex Network Models:

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(Preliminary) Experiments in Morphological Evolution

Richard Sproat University of Illinois at Urbana-Champaign rws@uiuc.edu 3rd Workshop on "Quantitative Investigations in Theoretical Linguistics" (QITL-3) Helsinki, 2-4 June 2008. (Preliminary) Experiments in Morphological Evolution. Overview. The explananda

757 views • 62 slides

Classes will begin shortly

557 views • 45 slides

AI on the Racetrack

AI on the Racetrack. By Emily Cohen. Hit the Track!. The first video racing game is said to be by Atari back in 1974 called Grand Track 10. Now:. Then:. Bad Racing AI. When competing against AI you should feel challenged Not so challenging

556 views • 43 slides

Degradable Transport Networks: Quantifying the Unquantifiable

Degradable Transport Networks: Quantifying the Unquantifiable. Professor Michael G H Bell ITLS, University of Sydney Business School. Scope of presentation. From Kobe 1995 to Christchurch 2011 Subjectivity of risk Societal risk averseness determines safety

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Complex System Dynamics LTCC Course Feb-March 2012

Complex System Dynamics LTCC Course Feb-March 2012. Dr Hannah Fry hfry@math.ucl.ac.uk. Degree Distribution. Uniform Lattice. Cumulative Degree Distribution. kmax = 4. Degree Distribution. Random Network. Cumulative Degree Distribution. kmax = 15, p(k) = exp(-k^2).

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ENG2000 Chapter 3 Crystals

ENG2000 Chapter 3 Crystals. Overview of chapter. In this chapter we seek to understand the types of crystal structures and their properties We also need to describe different directions and planes in crystals because the properties can be different in each direction

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Energy-Efficient Communication Protocol for Wireless Microsensor Networks

Energy-Efficient Communication Protocol for Wireless Microsensor Networks. Wendi Rabiner Heinzelman Anatha Chandrasakan Hari Balakrishnan Massachusetts Institute of Technology. Presented by Rick Skowyra. Overview. Introduction Radio Model Existing Protocols Direct Transmission

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Impact of Mobility and Heterogeneity on Coverage and Energy Consumption in Wireless Sensor Networks

Impact of Mobility and Heterogeneity on Coverage and Energy Consumption in Wireless Sensor Networks. Xiao Wang , Xinbing Wang , Jun Zhao Department of Electronic Engineering Shanghai Jiao Tong University, Shanghai, China. Outline. Introduction Background Related works Motivations

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Cascading failures in interdependent networks and financial systems -- Departmental Seminar

Cascading failures in interdependent networks and financial systems -- Departmental Seminar. Xuqing Huang Advisor: Prof. H. Eugene Stanley Collaborators: Prof. Shlomo Havlin Prof. Irena Vodenska Prof. Huijuan Wang

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Donghoon Shin, Saurabh Bagchi and Chih -Chun Wang

Donghoon Shin, Saurabh Bagchi and Chih -Chun Wang. Toward Optimal Sniffer-Channel Assignment for Reliable Monitoring in Multi-Channel Wireless Networks. Dependable Computing Systems Lab (DCSL) School of Electrical and Computer Engineering Purdue University. Outline.

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Bak-Sneppen Evolution models on Random and Scale-free Networks

Bak-Sneppen Evolution models on Random and Scale-free Networks. I. Introduction II. Random Neighbor Model III. BS Evolution Model on Network Structures IV. Results V. Summary. Sungmin Lee, Yup Kim Kyung Hee Univ. I. Introduction. The "punctuated equilibrium" theory.

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Observing Self-Organized Criticality - an approach to evolution dynamics

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Distributed Nuclear Norm Minimization for Matrix Completion

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Transport Properties of Fractal and Non-Fractal Scale-Free Networks

Transport Properties of Fractal and Non-Fractal Scale-Free Networks. M. Kitsak, S. Havlin, G. Paul, M. Riccaboni, F. Pammolli and H.E. Stanley . M. Kitsak et al, Phys Rev E, (in press), 2007. http://arxiv.org/abs/physics/0702001. Motivation and Objectives. Betweenness Centrality C

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Transport Properties of Fractal and Non-Fractal Scale-Free Networks

Transport Properties of Fractal and Non-Fractal Scale-Free Networks. M. Kitsak, S. Havlin, G. Paul, M. Riccaboni, F. Pammolli and H.E. Stanley . M. Kitsak et al, Phys Rev E, (in press), 2007. http://arxiv.org/abs/physics/0702001. Motivation and Objectives. Betweenness Centrality C

210 views • 7 slides

Analysis of FMD control strategies using contact network simulations

Analysis of FMD control strategies using contact network simulations Darren M. Green * , Istvan Z. Kiss, & Rowland R. Kao University of Oxford, Dept. of Zoology OX1 3PS. * darren.green@zoo.ox.ac.uk. Networks. Upper: low k , high D ; lower: high k , low D.

103 views • 1 slides