Social and Economic Networks: Lecture 1, Networks?


 Leslie Thornton
 2 years ago
 Views:
Transcription
1 Social and Economic Networks: Lecture 1, Networks? Alper Duman Izmir University Economics, February 26, 2013
2 Conventional economics assume that all agents are either completely connected or totally isolated. All agents are either homogeneous or there exist a representative agent All agents optimize without strategic substitutes or complementarities. Time and topology almost never matters
3 Figure : InputOutput Networks of US Economy,
4 Figure : ISE Firms Network via Interlocking Directors,2012 (Red: Koç group, Blues. Sabancı group)
5 Figure : Ownership Networks of Firms 2010
6 Figure : World Trade Network
7 Figure : Product Space Network
8 Figure : Subway Network
9 Types of Networks Directed Affiliation or TwoMode Weighted Multigraph Trees
10 Centrality Sabancı, Boyner, Yalçındağ, and Doğan families are all connected Centrality of the actors is very important! Power brokers and bridges; Who and where are they?
11 Figure : Marriage Network among Fortune 100
12 Centrality Which centrality is more important? 1. Degree Centrality 2. Closeness Centrality 3. Betweenness Centrality 4. RandomWalk Centrality 5. Eigenvector Centrality
13 Random Graphs and Networks Seminal model is due Erdös and Renyi (1959) Think about the number of possible networks with just 10 vertices Pick an edge probability 1 > p > 0, and choose any pair of vertices to apply. Do this for every pair. That is a graph G(N, p) with N vertices, and edge is present between any two vertices with a probability p.
14 Figure : Random Network with G(100, 0.01)
15 Figure : Random Network with G(100, 0.02)
16 Figure : Random Network with G(100, 0.03)
17 Characteristics of Random Networks Giant component emerges quickly Average path is very short Clustering is very low Edge formation is independent; very unlikely in social networks, WHY?
18 What is the probability of a fully connected network with N = 3? Three different events, each with a probability of p; so it should be p 3 What about the network with all isolated vertices? In general, any network with n vertices/nodes and m edges/links has a probability p m (1 p) n(n 1) 2 m to form
19 The probability that any given node i has d links is C(n 1; d)p d (1 p) n 1 d Fraction of nodes that have d links in a large network with large n and small p e (n 1)p ((n 1)p) d d! This is an approximation by a Binomial distribution
20 Figure : Frequency Distributions of Random Graphs frequency degree
21 What is the fraction of nodes that have zero degrees, that is the fraction of isolates? For large networks that would be approximated by e (n 1)p, if (n 1)p is sufficiently small If on average we expect only one isolate we have e (n 1)p = 1 n Solve this? How does it relate to the random network we have drawn previously for G(100, 0.02)?
22 Strategic Connection Model Agents form connections strategically There are (1) direct benefits of immediate links and (2) indirect benefits derived from friends of the friends There are costs of forming/keeping direct links Each agent shoud agree on the link; otherwise one is enough to severe the link
23 Figure : Selected 4Node Networks and Strategic Connection Model
24 Consider agent A in Network 1; the payoff would be δ + δ 2 + δ 3 c where δ is the direct benefit, and c is the cost. What about agent B? Which payoff is higher, A s or B s? Note that if there is no path between two vertices then there can not be any direct or indirect benefit.
25 Come up with your own example of a network Think about the motto of Six degrees of Separation ; why is it a small world? What if our network of connections are random, what would be the diameter in such a world? Why is the Star Network pairwisestable and efficient network for intermediate levels of cost of link formation?
Recap. Type of graphs Connectivity/Giant component Diameter Clustering coefficient Betweenness Centrality Degree distributions
Recap Type of graphs Connectivity/Giant component Diameter Clustering coefficient Betweenness Centrality Degree distributions Degree Distribution N k is the number of nodes with degree k P(k) is the probability
More informationIn the following we will only consider undirected networks.
Roles in Networks Roles in Networks Motivation for work: Let topology define network roles. Work by Kleinberg on directed graphs, used topology to define two types of roles: authorities and hubs. (Each
More informationSocial Media Mining. Graph Essentials
Graph Essentials Graph Basics Measures Graph and Essentials Metrics 2 2 Nodes and Edges A network is a graph nodes, actors, or vertices (plural of vertex) Connections, edges or ties Edge Node Measures
More informationSocial and Technological Network Analysis. Lecture 3: Centrality Measures. Dr. Cecilia Mascolo (some material from Lada Adamic s lectures)
Social and Technological Network Analysis Lecture 3: Centrality Measures Dr. Cecilia Mascolo (some material from Lada Adamic s lectures) In This Lecture We will introduce the concept of centrality and
More informationComplex Networks Analysis: Clustering Methods
Complex Networks Analysis: Clustering Methods Nikolai Nefedov Spring 2013 ISI ETH Zurich nefedov@isi.ee.ethz.ch 1 Outline Purpose to give an overview of modern graphclustering methods and their applications
More informationOption 1: empirical network analysis. Task: find data, analyze data (and visualize it), then interpret.
Programming project Task Option 1: empirical network analysis. Task: find data, analyze data (and visualize it), then interpret. Obtaining data This project focuses upon cocktail ingredients. Data was
More informationNetwork/Graph Theory. What is a Network? What is network theory? Graphbased representations. Friendship Network. What makes a problem graphlike?
What is a Network? Network/Graph Theory Network = graph Informally a graph is a set of nodes joined by a set of lines or arrows. 1 1 2 3 2 3 4 5 6 4 5 6 Graphbased representations Representing a problem
More informationA discussion of Statistical Mechanics of Complex Networks P. Part I
A discussion of Statistical Mechanics of Complex Networks Part I Review of Modern Physics, Vol. 74, 2002 Small Word Networks Clustering Coefficient ScaleFree Networks ErdösRényi model cover only parts
More informationNetwork Analysis and Visualization of Staphylococcus aureus. by Russ Gibson
Network Analysis and Visualization of Staphylococcus aureus by Russ Gibson Network analysis Based on graph theory Probabilistic models (random graphs) developed by Erdős and Rényi in 1959 Theory and tools
More informationGraph models for the Web and the Internet. Elias Koutsoupias University of Athens and UCLA. Crete, July 2003
Graph models for the Web and the Internet Elias Koutsoupias University of Athens and UCLA Crete, July 2003 Outline of the lecture Small world phenomenon The shape of the Web graph Searching and navigation
More informationMinimum Spanning Trees
Minimum Spanning Trees Algorithms and 18.304 Presentation Outline 1 Graph Terminology Minimum Spanning Trees 2 3 Outline Graph Terminology Minimum Spanning Trees 1 Graph Terminology Minimum Spanning Trees
More informationGraphs over Time Densification Laws, Shrinking Diameters and Possible Explanations
Graphs over Time Densification Laws, Shrinking Diameters and Possible Explanations Jurij Leskovec, CMU Jon Kleinberg, Cornell Christos Faloutsos, CMU 1 Introduction What can we do with graphs? What patterns
More informationCollecting Network Data in Surveys
Collecting Network Data in Surveys Arun Advani and Bansi Malde September 203 We gratefully acknowledge funding from the ESRCNCRM Node Programme Evaluation for Policy Analysis Grant reference RES576250042.
More informationSocial Media Mining. Network Measures
Klout Measures and Metrics 22 Why Do We Need Measures? Who are the central figures (influential individuals) in the network? What interaction patterns are common in friends? Who are the likeminded users
More informationSocial Network Mining
Social Network Mining Data Mining November 11, 2013 Frank Takes (ftakes@liacs.nl) LIACS, Universiteit Leiden Overview Social Network Analysis Graph Mining Online Social Networks Friendship Graph Semantics
More informationEcon 430 Lecture 9: Games on Networks
Alper Duman Izmir University Economics, May 10, 2013 SemiAnonymous Graphical Games refer to games on networks in which agents observe what their neighbours do The number of agents taking a specific action
More informationGraph definition Degree, in, out degree, oriented graph. Complete, regular, bipartite graph. Graph representation, connectivity, adjacency.
Mária Markošová Graph definition Degree, in, out degree, oriented graph. Complete, regular, bipartite graph. Graph representation, connectivity, adjacency. Isomorphism of graphs. Paths, cycles, trials.
More informationGraphs, Networks and Python: The Power of Interconnection. Lachlan Blackhall  lachlan@repositpower.com
Graphs, Networks and Python: The Power of Interconnection Lachlan Blackhall  lachlan@repositpower.com A little about me Graphs Graph, G = (V, E) V = Vertices / Nodes E = Edges NetworkX Native graph
More informationV. Adamchik 1. Graph Theory. Victor Adamchik. Fall of 2005
V. Adamchik 1 Graph Theory Victor Adamchik Fall of 2005 Plan 1. Basic Vocabulary 2. Regular graph 3. Connectivity 4. Representing Graphs Introduction A.Aho and J.Ulman acknowledge that Fundamentally, computer
More informationSystemic risk in derivative markets: a graphtheory analysis. D. Lautier & F. Raynaud
Systemic risk in derivative markets: a graphtheory analysis D. Lautier & F. Raynaud Objectives Empirical study on systemic risk in derivative markets Integration as a necessary condition for systemic
More informationPart 2: Community Detection
Chapter 8: Graph Data Part 2: Community Detection Based on Leskovec, Rajaraman, Ullman 2014: Mining of Massive Datasets Big Data Management and Analytics Outline Community Detection  Social networks 
More informationBioinformatics: Network Analysis
Bioinformatics: Network Analysis Graphtheoretic Properties of Biological Networks COMP 572 (BIOS 572 / BIOE 564)  Fall 2013 Luay Nakhleh, Rice University 1 Outline Architectural features Motifs, modules,
More informationCSV886: Social, Economics and Business Networks. Lecture 2: Affiliation and Balance. R Ravi ravi+iitd@andrew.cmu.edu
CSV886: Social, Economics and Business Networks Lecture 2: Affiliation and Balance R Ravi ravi+iitd@andrew.cmu.edu Granovetter s Puzzle Resolved Strong Triadic Closure holds in most nodes in social networks
More informationIndividual security and network design
Individual security and network design Diego Cerdeiro Marcin Dziubiński Sanjeev Goyal FIT 2015 Motivation Networks often face external threats in form of strategic or random attacks The attacks can be
More informationIntroduction to Networks and Business Intelligence
Introduction to Networks and Business Intelligence Prof. Dr. Daning Hu Department of Informatics University of Zurich Sep 17th, 2015 Outline Network Science A Random History Network Analysis Network Topological
More informationLecture 2.1 : The Distributed BellmanFord Algorithm. Lecture 2.2 : The Destination Sequenced Distance Vector (DSDV) protocol
Lecture 2 : The DSDV Protocol Lecture 2.1 : The Distributed BellmanFord Algorithm Lecture 2.2 : The Destination Sequenced Distance Vector (DSDV) protocol The Routing Problem S S D D The routing problem
More informationRandom graphs and complex networks
Random graphs and complex networks Remco van der Hofstad Honours Class, spring 2008 Complex networks Figure 2 Ye a s t p ro te in in te ra c tio n n e tw o rk. A m a p o f p ro tein p ro tein in tera c
More informationwww.objectivity.com An Introduction To Presented by Leon Guzenda, Founder, Objectivity
www.objectivity.com An Introduction To Graph Databases Presented by Leon Guzenda, Founder, Objectivity Mark Maagdenberg, Sr. Sales Engineer, Objectivity Paul DeWolf, Dir. Field Engineering, Objectivity
More informationAlgebra Tiles Activity 1: Adding Integers
Algebra Tiles Activity 1: Adding Integers NY Standards: 7/8.PS.6,7; 7/8.CN.1; 7/8.R.1; 7.N.13 We are going to use positive (yellow) and negative (red) tiles to discover the rules for adding and subtracting
More informationYear 9 mathematics test
Ma KEY STAGE 3 Year 9 mathematics test Tier 6 8 Paper 1 Calculator not allowed First name Last name Class Date Please read this page, but do not open your booklet until your teacher tells you to start.
More informationGeneral Network Analysis: Graphtheoretic. COMP572 Fall 2009
General Network Analysis: Graphtheoretic Techniques COMP572 Fall 2009 Networks (aka Graphs) A network is a set of vertices, or nodes, and edges that connect pairs of vertices Example: a network with 5
More informationATM Network Performance Evaluation And Optimization Using Complex Network Theory
ATM Network Performance Evaluation And Optimization Using Complex Network Theory Yalin LI 1, Bruno F. Santos 2 and Richard Curran 3 Air Transport and Operations Faculty of Aerospace Engineering The Technical
More informationSolutions to Exercises 8
Discrete Mathematics Lent 2009 MA210 Solutions to Exercises 8 (1) Suppose that G is a graph in which every vertex has degree at least k, where k 1, and in which every cycle contains at least 4 vertices.
More informationTopological Properties
Advanced Computer Architecture Topological Properties Routing Distance: Number of links on route Node degree: Number of channels per node Network diameter: Longest minimum routing distance between any
More informationCS5314 Randomized Algorithms. Lecture 16: Balls, Bins, Random Graphs (Random Graphs, Hamiltonian Cycles)
CS5314 Randomized Algorithms Lecture 16: Balls, Bins, Random Graphs (Random Graphs, Hamiltonian Cycles) 1 Objectives Introduce Random Graph Model used to define a probability space for all graphs with
More informationDistributed Computing over Communication Networks: Topology. (with an excursion to P2P)
Distributed Computing over Communication Networks: Topology (with an excursion to P2P) Some administrative comments... There will be a Skript for this part of the lecture. (Same as slides, except for today...
More informationYear 9 mathematics test
Ma KEY STAGE 3 Year 9 mathematics test Tier 5 7 Paper 1 Calculator not allowed First name Last name Class Date Please read this page, but do not open your booklet until your teacher tells you to start.
More informationHealthcare Analytics. Aryya Gangopadhyay UMBC
Healthcare Analytics Aryya Gangopadhyay UMBC Two of many projects Integrated network approach to personalized medicine Multidimensional and multimodal Dynamic Analyze interactions HealthMask Need for sharing
More informationBinomial trees and risk neutral valuation
Binomial trees and risk neutral valuation Moty Katzman September 19, 2014 Derivatives in a simple world A derivative is an asset whose value depends on the value of another asset. Call/Put European/American
More informationA MEASURE OF GLOBAL EFFICIENCY IN NETWORKS. Aysun Aytac 1, Betul Atay 2. Faculty of Science Ege University 35100, Bornova, Izmir, TURKEY
International Journal of Pure and Applied Mathematics Volume 03 No. 05, 670 ISSN: 38080 (printed version); ISSN: 343395 (online version) url: http://www.ijpam.eu doi: http://dx.doi.org/0.73/ijpam.v03i.5
More informationRational Functions ( )
Rational Functions A rational function is a function of the form r P Q where P and Q are polynomials. We assume that P() and Q() have no factors in common, and Q() is not the zero polynomial. The domain
More informationHomework 15 Solutions
PROBLEM ONE (Trees) Homework 15 Solutions 1. Recall the definition of a tree: a tree is a connected, undirected graph which has no cycles. Which of the following definitions are equivalent to this definition
More informationALGEBRA I A PLUS COURSE OUTLINE
ALGEBRA I A PLUS COURSE OUTLINE OVERVIEW: 1. Operations with Real Numbers 2. Equation Solving 3. Word Problems 4. Inequalities 5. Graphs of Functions 6. Linear Functions 7. Scatterplots and Lines of Best
More informationAn AgentBased Adaptation of Friendship Games: Observations on Network Topologies
An AgentBased Adaptation of Friendship Games: Observations on Network Topologies David S. Dixon University of New Mexico, Albuquerque NM 87131, USA Abstract. A friendship game in game theory is a network
More information(a) (b) (c) Figure 1 : Graphs, multigraphs and digraphs. If the vertices of the leftmost figure are labelled {1, 2, 3, 4} in clockwise order from
4 Graph Theory Throughout these notes, a graph G is a pair (V, E) where V is a set and E is a set of unordered pairs of elements of V. The elements of V are called vertices and the elements of E are called
More informationMining SocialNetwork Graphs
342 Chapter 10 Mining SocialNetwork Graphs There is much information to be gained by analyzing the largescale data that is derived from social networks. The bestknown example of a social network is
More informationCircuits 1 M H Miller
Introduction to Graph Theory Introduction These notes are primarily a digression to provide general background remarks. The subject is an efficient procedure for the determination of voltages and currents
More informationPrinciple of (Weak) Mathematical Induction. P(1) ( n 1)(P(n) P(n + 1)) ( n 1)(P(n))
Outline We will cover (over the next few weeks) Mathematical Induction (or Weak Induction) Strong (Mathematical) Induction Constructive Induction Structural Induction Principle of (Weak) Mathematical Induction
More informationChapter 2: Systems of Linear Equations and Matrices:
At the end of the lesson, you should be able to: Chapter 2: Systems of Linear Equations and Matrices: 2.1: Solutions of Linear Systems by the Echelon Method Define linear systems, unique solution, inconsistent,
More informationGraph Algorithms using MapReduce
Graph Algorithms using MapReduce Graphs are ubiquitous in modern society. Some examples: The hyperlink structure of the web 1/7 Graph Algorithms using MapReduce Graphs are ubiquitous in modern society.
More informationOutline. NPcompleteness. When is a problem easy? When is a problem hard? Today. Euler Circuits
Outline NPcompleteness Examples of Easy vs. Hard problems Euler circuit vs. Hamiltonian circuit Shortest Path vs. Longest Path 2pairs sum vs. general Subset Sum Reducing one problem to another Clique
More informationGraph Theory and Complex Networks: An Introduction. Chapter 06: Network analysis
Graph Theory and Complex Networks: An Introduction Maarten van Steen VU Amsterdam, Dept. Computer Science Room R4.0, steen@cs.vu.nl Chapter 06: Network analysis Version: April 8, 04 / 3 Contents Chapter
More informationCombinatorics: The Fine Art of Counting
Combinatorics: The Fine Art of Counting Week 9 Lecture Notes Graph Theory For completeness I have included the definitions from last week s lecture which we will be using in today s lecture along with
More informationSome questions... Graphs
Uni Innsbruck Informatik  1 Uni Innsbruck Informatik  2 Some questions... Peerto topeer Systems Analysis of unstructured P2P systems How scalable is Gnutella? How robust is Gnutella? Why does FreeNet
More informationFollow links for Class Use and other Permissions. For more information send email to: permissions@press.princeton.edu
COPYRIGHT NOTICE: Matthew O. Jackson: Social and Economic Networks is published by Princeton University Press and copyrighted, 2008, by Princeton University Press. All rights reserved. No part of this
More informationApplication of Graph Theory to
Application of Graph Theory to Requirements Traceability A methodology for visualization of large requirements sets Sam Brown L3 Communications This presentation consists of L3 STRATIS general capabilities
More informationGraph Theory. Directed and undirected graphs make useful mental models for many situations. These objects are loosely defined as follows:
Graph Theory Directed and undirected graphs make useful mental models for many situations. These objects are loosely defined as follows: Definition An undirected graph is a (finite) set of nodes, some
More informationAbout the Tutorial. Audience. Prerequisites. Disclaimer & Copyright
About the Tutorial This tutorial offers a brief introduction to the fundamentals of graph theory. Written in a readerfriendly style, it covers the types of graphs, their properties, trees, graph traversability,
More informationWhy graph clustering is useful?
Graph Clustering Why graph clustering is useful? Distance matrices are graphs as useful as any other clustering Identification of communities in social networks Webpage clustering for better data management
More informationData Structures and Algorithms Written Examination
Data Structures and Algorithms Written Examination 22 February 2013 FIRST NAME STUDENT NUMBER LAST NAME SIGNATURE Instructions for students: Write First Name, Last Name, Student Number and Signature where
More informationLecture 8 : Coordinate Geometry. The coordinate plane The points on a line can be referenced if we choose an origin and a unit of 20
Lecture 8 : Coordinate Geometry The coordinate plane The points on a line can be referenced if we choose an origin and a unit of 0 distance on the axis and give each point an identity on the corresponding
More informationChapter 15: Distributed Structures. Topology
1 1 Chapter 15: Distributed Structures Topology Network Types Operating System Concepts 15.1 Topology Sites in the system can be physically connected in a variety of ways; they are compared with respect
More informationGraph Mining Techniques for Social Media Analysis
Graph Mining Techniques for Social Media Analysis Mary McGlohon Christos Faloutsos 1 11 What is graph mining? Extracting useful knowledge (patterns, outliers, etc.) from structured data that can be represented
More informationPractical statistical network analysis (with R and igraph)
Practical statistical network analysis (with R and igraph) Gábor Csárdi csardi@rmki.kfki.hu Department of Biophysics, KFKI Research Institute for Nuclear and Particle Physics of the Hungarian Academy of
More informationPerformance of networks containing both MaxNet and SumNet links
Performance of networks containing both MaxNet and SumNet links Lachlan L. H. Andrew and Bartek P. Wydrowski Abstract Both MaxNet and SumNet are distributed congestion control architectures suitable for
More informationLecture 9. Sergei Fedotov. 20912  Introduction to Financial Mathematics. Sergei Fedotov (University of Manchester) 20912 2010 1 / 8
Lecture 9 Sergei Fedotov 20912  Introduction to Financial Mathematics Sergei Fedotov (University of Manchester) 20912 2010 1 / 8 Lecture 9 1 RiskNeutral Valuation 2 RiskNeutral World 3 TwoSteps Binomial
More informationCMPSCI611: Approximating MAXCUT Lecture 20
CMPSCI611: Approximating MAXCUT Lecture 20 For the next two lectures we ll be seeing examples of approximation algorithms for interesting NPhard problems. Today we consider MAXCUT, which we proved to
More informationThe mathematics of networks
The mathematics of networks M. E. J. Newman Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109 1040 In much of economic theory it is assumed that economic agents interact,
More informationNetwork Metrics, Planar Graphs, and Software Tools. Based on materials by Lala Adamic, UMichigan
Network Metrics, Planar Graphs, and Software Tools Based on materials by Lala Adamic, UMichigan Network Metrics: Bowtie Model of the Web n The Web is a directed graph: n webpages link to other webpages
More informationTopic 5 Review [81 marks]
Topic 5 Review [81 marks] A foursided die has three blue faces and one red face. The die is rolled. Let B be the event a blue face lands down, and R be the event a red face lands down. 1a. Write down
More informationCourse: Model, Learning, and Inference: Lecture 5
Course: Model, Learning, and Inference: Lecture 5 Alan Yuille Department of Statistics, UCLA Los Angeles, CA 90095 yuille@stat.ucla.edu Abstract Probability distributions on structured representation.
More informationNodeXL for Network analysis Demo/handson at NICAR 2012, St Louis, Feb 24. Peter Aldhous, San Francisco Bureau Chief. peter@peteraldhous.
NodeXL for Network analysis Demo/handson at NICAR 2012, St Louis, Feb 24 Peter Aldhous, San Francisco Bureau Chief peter@peteraldhous.com NodeXL is a template for Microsoft Excel 2007 and 2010, which
More informationApplying Social Network Analysis to the Information in CVS Repositories
Applying Social Network Analysis to the Information in CVS Repositories Luis LopezFernandez, Gregorio Robles, Jesus M. GonzalezBarahona GSyC, Universidad Rey Juan Carlos {llopez,grex,jgb}@gsyc.escet.urjc.es
More informationGraph Theory and Networks in Biology
Graph Theory and Networks in Biology Oliver Mason and Mark Verwoerd March 14, 2006 Abstract In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss
More informationA Comparison of Frontier and Emerging Capital Market Networks Margaret Moten Daniel Evans
A Comparison of Frontier and Emerging Capital Market Networks Margaret Moten Daniel Evans Network Science Center daniel.evans@usma.edu www.netscience.usma.edu Why Capital Market Networks? Developed capital
More informationSocial Network Analysis: Introduzione all'analisi di reti sociali
Social Network Analysis: Introduzione all'analisi di reti sociali Michele Coscia Dipartimento di Informatica Università di Pisa www.di.unipi.it/~coscia Piano Lezioni Introduzione Misure + Modelli di Social
More informationMathematical Modelling Lecture 8 Networks
Lecture 8 Networks phil.hasnip@york.ac.uk Overview of Course Model construction dimensional analysis Experimental input fitting Finding a best answer optimisation Tools for constructing and manipulating
More informationPlanarity Planarity
Planarity 8.1 71 Planarity Up until now, graphs have been completely abstract. In Topological Graph Theory, it matters how the graphs are drawn. Do the edges cross? Are there knots in the graph structure?
More informationGraphs and Network Flows IE411 Lecture 1
Graphs and Network Flows IE411 Lecture 1 Dr. Ted Ralphs IE411 Lecture 1 1 References for Today s Lecture Required reading Sections 17.1, 19.1 References AMO Chapter 1 and Section 2.1 and 2.2 IE411 Lecture
More informationOn the Value of a Social Network
On the Value of a Social Network Sandeep Chalasani Abstract Different laws have been proposed for the value of a social network. According to Metcalfe s law, the value of a network is proportional to n
More informationOnline Social Networks and Network Economics. Aris Anagnostopoulos, Online Social Networks and Network Economics
Online Social Networks and Network Economics Who? Dr. Luca Becchetti Prof. Elias Koutsoupias Prof. Stefano Leonardi What will We Cover? Possible topics: Structure of social networks Models for social networks
More informationAn Introduction to APGL
An Introduction to APGL Charanpal Dhanjal February 2012 Abstract Another Python Graph Library (APGL) is a graph library written using pure Python, NumPy and SciPy. Users new to the library can gain an
More informationNETWORK SCIENCE RANDOM NETWORKS
3 ALBERTLÁSZLÓ BARABÁSI NETWORK SCIENCE ACKNOWLEDGEMENTS MÁRTON PÓSFAI GABRIELE MUSELLA MAURO MARTINO ROBERTA SINATRA SARAH MORRISON AMAL HUSSEINI PHILIPP HOEVEL INDEX Introduction 1 The Random Networ
More informationSample Problems in Discrete Mathematics
Sample Problems in Discrete Mathematics This handout lists some sample problems that you should be able to solve as a prerequisite to Computer Algorithms Try to solve all of them You should also read
More informationPlanar Tree Transformation: Results and Counterexample
Planar Tree Transformation: Results and Counterexample Selim G Akl, Kamrul Islam, and Henk Meijer School of Computing, Queen s University Kingston, Ontario, Canada K7L 3N6 Abstract We consider the problem
More informationSampling Biases in IP Topology Measurements
Sampling Biases in IP Topology Measurements Anukool Lakhina with John Byers, Mark Crovella and Peng Xie Department of Boston University Discovering the Internet topology Goal: Discover the Internet Router
More informationGraph Mining and Social Network Analysis
Graph Mining and Social Network Analysis Data Mining and Text Mining (UIC 583 @ Politecnico di Milano) References Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", The Morgan Kaufmann
More informationDistance Degree Sequences for Network Analysis
Universität Konstanz Computer & Information Science Algorithmics Group 15 Mar 2005 based on Palmer, Gibbons, and Faloutsos: ANF A Fast and Scalable Tool for Data Mining in Massive Graphs, SIGKDD 02. Motivation
More informationWhy? A central concept in Computer Science. Algorithms are ubiquitous.
Analysis of Algorithms: A Brief Introduction Why? A central concept in Computer Science. Algorithms are ubiquitous. Using the Internet (sending email, transferring files, use of search engines, online
More informationMy work provides a distinction between the national inputoutput model and three spatial models: regional, interregional y multiregional
Mexico, D. F. 25 y 26 de Julio, 2013 My work provides a distinction between the national inputoutput model and three spatial models: regional, interregional y multiregional Walter Isard (1951). Outline
More informationDATA ANALYSIS IN PUBLIC SOCIAL NETWORKS
International Scientific Conference & International Workshop Present Day Trends of Innovations 2012 28 th 29 th May 2012 Łomża, Poland DATA ANALYSIS IN PUBLIC SOCIAL NETWORKS Lubos Takac 1 Michal Zabovsky
More informationCluster detection algorithm in neural networks
Cluster detection algorithm in neural networks David Meunier and Hélène PaugamMoisy Institute for Cognitive Science, UMR CNRS 5015 67, boulevard Pinel F69675 BRON  France Email: {dmeunier,hpaugam}@isc.cnrs.fr
More informationCALCULATING THE AREA OF A FLOWER BED AND CALCULATING NUMBER OF PLANTS NEEDED
This resource has been produced as a result of a grant awarded by LSIS. The grant was made available through the Skills for Life Support Programme in 2010. The resource has been developed by (managers
More informationNetwork Analysis of a Large Scale Open Source Project
2014 40th Euromicro Conference on Software Engineering and Advanced Applications Network Analysis of a Large Scale Open Source Project Alma OručevićAlagić, Martin Höst Department of Computer Science,
More informationWORKSHOP Analisi delle Reti Sociali per conoscere uno strumento uno strumento per conoscere
Università di Salerno WORKSHOP Analisi delle Reti Sociali per conoscere uno strumento uno strumento per conoscere The scientific collaboration network of the University of Salerno Michele La Rocca, Giuseppe
More information1. Introduction Gene regulation Genomics and genome analyses Hidden markov model (HMM)
1. Introduction Gene regulation Genomics and genome analyses Hidden markov model (HMM) 2. Gene regulation tools and methods Regulatory sequences and motif discovery TF binding sites, microrna target prediction
More informationLecture Notes on Spanning Trees
Lecture Notes on Spanning Trees 15122: Principles of Imperative Computation Frank Pfenning Lecture 26 April 26, 2011 1 Introduction In this lecture we introduce graphs. Graphs provide a uniform model
More informationSix Degrees of Separation in Online Society
Six Degrees of Separation in Online Society Lei Zhang * TsinghuaSouthampton Joint Lab on Web Science Graduate School in Shenzhen, Tsinghua University Shenzhen, Guangdong Province, P.R.China zhanglei@sz.tsinghua.edu.cn
More informationCoach Monks s MathCounts Playbook! Secret facts that every Mathlete should know in order to win!
Coach Monks s MathCounts Playbook! Secret facts that every Mathlete should know in order to win! Learn the items marked with a first. Then once you have mastered them try to learn the other topics.. Squares
More informationCharacter Image Patterns as Big Data
22 International Conference on Frontiers in Handwriting Recognition Character Image Patterns as Big Data Seiichi Uchida, Ryosuke Ishida, Akira Yoshida, Wenjie Cai, Yaokai Feng Kyushu University, Fukuoka,
More information