Network Analysis For Sustainability Management
|
|
- Nickolas Morgan
- 7 years ago
- Views:
Transcription
1 Network Analysis For Sustainability Management 1 Cátia Vaz 1º Summer Course in E4SD
2 Outline Motivation Networks representation Structural network analysis Behavior network analysis 2
3 Networks Over the past decade has been a growing public fascination with the complex connectedness of modern society. The connectedness: Aggregate behavior of groups of people; Reflects the ways in which our decisions can have subtle consequences for others. 3
4 A network is Networks A pattern of interconnections among a set of things More precisely. A collection of objects in which some pair of objects are connected by links 4
5 Network Example 1: 5
6 Network Example 2: 6
7 Network Example 3: 7
8 Network Connectedness Network connectedness at two levels Structure level Who is linked to whom Behavior level Each individual s actions have implicit consequences for the outcomes of everyone in the system For analysing the network connectedness and inferring properties, we rely on Graph theory Game theory 8
9 Structural level analysis Detection of strong/weak ties Individuals centrality determine the relative importance of a individual within the graph Betweenness centrality, Closeness centrality, degree centrality Captures different aspects of the relevance of an individual Communities detection 9
10 10 Structural Analysis Example: unclecj.blogspot.com
11 Behavior level analysis How a group of people must simultaneously choose to act, knowing that the outcome will depend on the decisions made by all of them? 11 Traffic Network Example:
12 Strategic Interaction in Networks We can combine graph theory and game theory to produce richer models of behavior in networks 12 Usually, the network structure encodes a lot about the pattern of trade The success levels of different participants are affected by their positions in the network What factors determine a powerful position on networks?
13 Strategic Interaction in Networks 13 Seaching in Twitter within the topic new Finance Minister in Portugal
14 Network Dynamics: Population Effects The way in which new practices spread through a population depends in large part on the fact that people influence each other s behavior. This is a central issue for understanding networks and aggregate behavior 14 Taking network structure into account provides further insights into how such kinds of influence take place!
15 Network Dynamics: Population/Structural Effects When individuals have incentives to adopt the behavior of their neighbors there can be: Cascading Effects 15 Example: recycling in Portugal Sociedade Ponto Verde Video
16 Outline Motivation Networks representation Structural network analysis Behavior network analysis 16
17 Network Characterization 17 All networks consists of two primary building blocks: Vertices (or nodes, agents,..) Edges (or ties, arcs, connections, ) Networks Vertices Edges Twitter Users Follower/Following; Mentions; Replies Facebook Friends Friendship Relations Skype Contacts Messages/Conversations Traffic Cross Roads
18 Edges can be: Undirected Are reciprocated Directed Are asymmetric Network Characterization Have a origin and a destination Networks Vertices Edges Twitter Users Follower (directed) Facebook Friends Friendship Relations (undirected) Skype Contacts Messages/Conversations (undirected) Traffic Cross Roads (directed) 18
19 Network Characterization Edges can be classified into: Weighted 19 A edge has an associated value that indicate the strength or frequency of a tie Unweighted Only indicates if an edge exists or not Can be seen as a binary edge (with zero or one value) Networks Vertices Edges Twitter Users Follower (unweighted) Facebook Friends Friendship Relations (unweighted) Skype Contacts Messages/Conversations (weighted/unweighed) Traffic Cross Roads (usually weighted)
20 Graphs A graph is a way of specifying relationships among a set of nodes The relationships are the edges A graph serve as a mathematical model of network structures 20
21 Graph Example 1 Consider that Ann is a friend of Carol and Bob and Bob is a friend of Alice. How to visualize this graph? 21 Undirected and Unweighted Network
22 Graph Example 2 22 Consider the bideractional avenue Avenida da Liberdade between Marquês de Pombal and Rossio; the undirectional road Rua Braamcamp between Marquês de Pombal and the Praça Castilho and the undirectional road Rua Lisboa between Praça Castilho and Rossio. Directed and Weighted Network Q: What do values represents?
23 Network Representation 23 As a matrix Ann Bob Carol Alice Ann Bob Carol Alice As an Edge List Vertex 1 Vertex 2 Ann Bob Ann Carol Bob Ann Bob Alice Carol Ann Alice Bob
24 Network Representation 24 As a matrix Praça Castilho Marques De Pombal Praça Castilho Marques de Pombal Rossio Rossio As an Edge List Vertex 1 Vertex 2 Label Praça Castilho Marques de Pombal Marques de Pombal Rossio Rossio 600 Rossio 1600 Praça Castilho Marques de Pombal Q:What are the differences between this and the previous one?
25 Paths A path is a sequence of nodes with the property that each consecutive pair in the sequence is connected by an edge. 25 Q: Are there paths in our networks examples?
26 Connectivity and Connected Components A graph is connected if for every pair of nodes there is a path between them Not all graphs are connected! A connected component of a graph is a subset of nodes that: 26 Property 1: every node in the subset has a path to every other Property 2: is not contained in another subset with property 1. Q: What are our in network examples the connected components?
27 Small-World Phenomenon: are we really so near from each other? When analysing the connected component of large networks: Presumably the global network is not connected! Think about a social network Still, they have a giant component 27 A connected component that contains a significant fraction of all nodes The small work phenomenon occurs in the giant component of the network!
28 Small-World Phenomenon: are we really so near from each other? 28 Milgram Exp. MSN Facebook Twitter Twitter Vertex person user user user user Link selected person message exchange (conversation) friendship follower mention Symmetric no yes yes no no Avg. dist
29 Outline 29 Motivation Networks representation Structural network analysis Behavior network analysis
30 Triadic Closure It is also useful to think about how a network evolves over time An important principle to take into account is If two people in a social network have a friend in common, then there is an increased likelihood that they will become friends themselves at some point in the fuure (Anatole Rapaport 1953) Designated as Triadic Closure Reasons for triadic closure B Opportunity Trusting Incentive To capture is prevalence we can use the clustering coefficient A C 30
31 Bridges 31 C A B L D E K O An edge that joins nodes A and B in a graph is called a bridge if deleting the edge would cause A an B to lie in two different components. Q: Does bridges occurs in real social networks?
32 Local Bridges J G 32 M F N C A B L D E K O An edge that joins nodes A and B in a graph is called a local bridge if A and B does not have friends in common. Deleting such an edge would increase distance between A and B. Q: Does local bridges occurs in real social networks?
33 The strenght of weak ties Stronger links represents closer friendships and greater frequency of interaction We can classify links in: Stronger ties (corresponding to friends) Weaker ties (corresponding to acquantances) Thus, under the assumption of Triadic Closure property and a sufficient number of strong ties Local Bridges are necessary weak ties Therefore, weak ties: Connect to new sources of information, new opportunities Allow to reach other parts of the network 33
34 Betweeness Centrality Betweeness Centrality of a node is: 34 The number of shortest paths from all vertices to all others that pass through that node This measurement can be seen as a kind of a bridge score, since it measures: how much removing an individual of the network would disrupt the connections between other individuals in the network. The bigger is the betweeness centrality value for a node => The bigger is its importance as a broker between two otherwise separated groups
35 Example 1: Betweeness 35 Seaching in Twitter within the topic new Finance Minister in Portugal
36 Example 2:Betweeness 36 Seaching in Twitter within the topic: comprar produtos nacionais
37 Closeness Centrality The closeness centrality of a node is 37 the average distance between the node and the other nodes of the network Can be seen as a measurement of how close each individual is to the other individuals in the network The lower the closeness centrality value is => the more near this node is from the others
38 Example 1: Closeness 38 Seaching in Twitter Within the topic new Finance Minister in Portugal
39 Example 2: Closeness 39 Seaching in Twitter within the topic: comprar produtos nacionais
40 Communities detection A community in a network is a subset of the network nodes such that is densely connected internally 40 Q: How many communities in this network?
41 Communities detection And in this network, how many communities are? 41 A divisive method proposed by Newman and Girvan has been widely applied to networks, in particular to social networks.
42 Outline 42 Motivation Networks representation Structural network analysis Behavior network analysis
43 Diffusion in networks 43 When we want to analyze the processes by which new ideas and innovations are adopted by a population, the underlying social network can be considered at two different levels: (Level 1): The level of seeing the network as a relatively amorphous population of individual and look at effects in aggregate (Level 2): The level of seeing closer to the fine structure of the network, looking at how individuals are influenced by the particular network neighbors Level 1 Level 2 Network models Population models Structural models
44 Diffusions of ideas as behaviors There are clear connections between epidemic disease and the diffusion of ideas through social networks: 44 Both diseases and ideas can spread from one person to another It is known as social contagion With social contagion People make decisions on adopting a new idea or innovation SI, SIS and SIR are epidemical models Belong to the structural model group (level 2)
45 SI, SIS and SIR model 45 SI model SIS model S S λ λ I I S->Susceptible or Ignorant I -> Spreader R ->Stiflers (informed agents who don t spread information) δ SIR model S λ I δ R λ: contact infection rate δ: recovery rate
46 SI model 46 S λ I <k> : average number of contacts of a given individual x : fraction of infected in the population 1-x : fraction of susceptible λ<k> : Spread rate / force of infection i.e., an infected individual is able to transmit the disease with λ<k> others per unit time
47 SIS model 47 λ S I δ <k> : average number of contacts of a given individual x : fraction of infected in the population 1-x : fraction of susceptible λ<k>: Spread rate / force of infection δ: recovery rate
48 But the complexity of social behavior Spread of information, adoption of new trends, habits, opinions, etc., are all intentional acts, unlike disease spreading. Some behaviors, trends and ideas may bring more benefits than others... For instance, we are free to choose among different opinions and behaviors, or even create new ones... Contrary to disease spreading, there s much more around than contact processes. 48
49 How rational are we? 49
50 The role of social networks 50
51 Happiness is contagious 51 On average, the likelihood of I feel happy increases by 15% if I have a happy friend (distance 1)! Increases 10% if a friend of a friend happy (distance 2)! Increases 5% if a friend of a friend of a friend happy (distance 3)! Each unhappy friend decreases 7% this probability! Christakis & Fowler, NEJM, 2007, 2008
52 Game theory & rational behavior The cost-benefit dilemma: 52 Donor Pays a cost c Receiver Receives a benefit b If both play as a donor and as a receiver Rational Goal : Maximize your own payoff: If your opponent plays C: you better play D If your opponent plays D: you better play D But: CC is better than DD you your opponent C D C b-c -c D b 0 General Dilemma: Despite mutual cooperation (CC) being better than mutual defection (DD), individual rational choice evolves to (DD)
53 All symmetric 2-person dilemmas of cooperation symmetric 2-player games : 2 individuals meet 53 each player uses 1 of 2 strategies ( Cooperate or Defect ) each possible outcome has an associated payoff (tabulated in the payoff-matrix) R: mutual cooperation P : mutual defection S : sucker s payoff T : temptation to defect you your opponent C D C R S D T P
54 Case study: Recycling in Portugal 54 A buddy on the school A children go green Not go green go green b 0 not go green 0 0
55 For multiple players 55
56 Case study: Recycling in Portugal 56
57 For scale-free networks 57
58 Bibliography 58 David Easley and Jon Kleinberg, Networks and Markets: Reasoning about a Highly Connected World, Cambridge University press, 2010 Derek L. Hansen, Ben Shneiderman and Marc A. Smith: Analysing Social Media Networks with NodeXL: Insights from a Connected World, Elsevier,2011 Christakis & Fowler, The Collective Dynamics of Smoking in a Large Social Network, NEJM, 2007, 2008 Christakis & Fowler, Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study, NEJM, 2007, 2008
59 Thanks Professor Adjunto PhD 59
Strong and Weak Ties
Strong and Weak Ties Web Science (VU) (707.000) Elisabeth Lex KTI, TU Graz April 11, 2016 Elisabeth Lex (KTI, TU Graz) Networks April 11, 2016 1 / 66 Outline 1 Repetition 2 Strong and Weak Ties 3 General
More informationExtracting Information from Social Networks
Extracting Information from Social Networks Aggregating site information to get trends 1 Not limited to social networks Examples Google search logs: flu outbreaks We Feel Fine Bullying 2 Bullying Xu, Jun,
More informationBig Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network
, pp.273-284 http://dx.doi.org/10.14257/ijdta.2015.8.5.24 Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network Gengxin Sun 1, Sheng Bin 2 and
More information1. Write the number of the left-hand item next to the item on the right that corresponds to it.
1. Write the number of the left-hand item next to the item on the right that corresponds to it. 1. Stanford prison experiment 2. Friendster 3. neuron 4. router 5. tipping 6. small worlds 7. job-hunting
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 like-minded users
More informationNetwork Analysis Basics and applications to online data
Network Analysis Basics and applications to online data Katherine Ognyanova University of Southern California Prepared for the Annenberg Program for Online Communities, 2010. Relational data Node (actor,
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 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 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 informationNetwork-Based Tools for the Visualization and Analysis of Domain Models
Network-Based Tools for the Visualization and Analysis of Domain Models Paper presented as the annual meeting of the American Educational Research Association, Philadelphia, PA Hua Wei April 2014 Visualizing
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 informationBusiness Intelligence and Process Modelling
Business Intelligence and Process Modelling F.W. Takes Universiteit Leiden Lecture 7: Network Analytics & Process Modelling Introduction BIPM Lecture 7: Network Analytics & Process Modelling Introduction
More informationWhat is SNA? A sociogram showing ties
Case Western Reserve University School of Medicine Social Network Analysis: Nuts & Bolts Papp KK 1, Zhang GQ 2 1 Director, Program Evaluation, CTSC, 2 Professor, Electrical Engineering and Computer Science,
More informationSociology and CS. Small World. Sociology Problems. Degree of Separation. Milgram s Experiment. How close are people connected? (Problem Understanding)
Sociology Problems Sociology and CS Problem 1 How close are people connected? Small World Philip Chan Problem 2 Connector How close are people connected? (Problem Understanding) Small World Are people
More informationComplex Network Analysis of Brain Connectivity: An Introduction LABREPORT 5
Complex Network Analysis of Brain Connectivity: An Introduction LABREPORT 5 Fernando Ferreira-Santos 2012 Title: Complex Network Analysis of Brain Connectivity: An Introduction Technical Report Authors:
More informationMINFS544: Business Network Data Analytics and Applications
MINFS544: Business Network Data Analytics and Applications March 30 th, 2015 Daning Hu, Ph.D., Department of Informatics University of Zurich F Schweitzer et al. Science 2009 Stop Contagious Failures in
More informationDIGITS CENTER FOR DIGITAL INNOVATION, TECHNOLOGY, AND STRATEGY THOUGHT LEADERSHIP FOR THE DIGITAL AGE
DIGITS CENTER FOR DIGITAL INNOVATION, TECHNOLOGY, AND STRATEGY THOUGHT LEADERSHIP FOR THE DIGITAL AGE INTRODUCTION RESEARCH IN PRACTICE PAPER SERIES, FALL 2011. BUSINESS INTELLIGENCE AND PREDICTIVE ANALYTICS
More informationApplying Social Network Analysis to the Information in CVS Repositories
Applying Social Network Analysis to the Information in CVS Repositories Luis Lopez-Fernandez, Gregorio Robles, Jesus M. Gonzalez-Barahona GSyC, Universidad Rey Juan Carlos {llopez,grex,jgb}@gsyc.escet.urjc.es
More informationLecture 17 : Equivalence and Order Relations DRAFT
CS/Math 240: Introduction to Discrete Mathematics 3/31/2011 Lecture 17 : Equivalence and Order Relations Instructor: Dieter van Melkebeek Scribe: Dalibor Zelený DRAFT Last lecture we introduced the notion
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 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 informationProtein Protein Interaction Networks
Functional Pattern Mining from Genome Scale Protein Protein Interaction Networks Young-Rae Cho, Ph.D. Assistant Professor Department of Computer Science Baylor University it My Definition of Bioinformatics
More informationPractical Graph Mining with R. 5. Link Analysis
Practical Graph Mining with R 5. Link Analysis Outline Link Analysis Concepts Metrics for Analyzing Networks PageRank HITS Link Prediction 2 Link Analysis Concepts Link A relationship between two entities
More informationSOCIAL NETWORK ANALYSIS. ANZEA conference 2012 Jenny Long
SOCIAL NETWORK ANALYSIS ANZEA conference 2012 Jenny Long BACKGROUND: BEHAVIOUR CONTAGION / DIFFUSION OF INNOVATION Other examples: information, innovation, collaboration, communication, resource sharing
More informationCluster detection algorithm in neural networks
Cluster detection algorithm in neural networks David Meunier and Hélène Paugam-Moisy Institute for Cognitive Science, UMR CNRS 5015 67, boulevard Pinel F-69675 BRON - France E-mail: {dmeunier,hpaugam}@isc.cnrs.fr
More informationIC05 Introduction on Networks &Visualization Nov. 2009. <mathieu.bastian@gmail.com>
IC05 Introduction on Networks &Visualization Nov. 2009 Overview 1. Networks Introduction Networks across disciplines Properties Models 2. Visualization InfoVis Data exploration
More informationDiscrete Mathematics & Mathematical Reasoning Chapter 10: Graphs
Discrete Mathematics & Mathematical Reasoning Chapter 10: Graphs Kousha Etessami U. of Edinburgh, UK Kousha Etessami (U. of Edinburgh, UK) Discrete Mathematics (Chapter 6) 1 / 13 Overview Graphs and Graph
More informationGraph theoretic approach to analyze amino acid network
Int. J. Adv. Appl. Math. and Mech. 2(3) (2015) 31-37 (ISSN: 2347-2529) Journal homepage: www.ijaamm.com International Journal of Advances in Applied Mathematics and Mechanics Graph theoretic approach to
More informationModel for simulating mechanisms responsible of similarities between people connected in networks of social relations
Model for simulating mechanisms responsible of similarities between people connected in networks of social relations Błażej Żak 1 and Anita Zbieg 2 1 Wrocław University of Technology, Wroclaw, Poland blazej.zak@pwr.wroc.pl
More informationNodes, Ties and Influence
Nodes, Ties and Influence Chapter 2 Chapter 2, Community Detec:on and Mining in Social Media. Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. 1 IMPORTANCE OF NODES 2 Importance of Nodes Not
More informationNetwork (Tree) Topology Inference Based on Prüfer Sequence
Network (Tree) Topology Inference Based on Prüfer Sequence C. Vanniarajan and Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai 600036 vanniarajanc@hcl.in,
More informationIn the situations that we will encounter, we may generally calculate the probability of an event
What does it mean for something to be random? An event is called random if the process which produces the outcome is sufficiently complicated that we are unable to predict the precise result and are instead
More informationThe Open University s repository of research publications and other research outputs
Open Research Online The Open University s repository of research publications and other research outputs Online survey for collective clustering of computer generated architectural floor plans Conference
More informationSGL: Stata graph library for network analysis
SGL: Stata graph library for network analysis Hirotaka Miura Federal Reserve Bank of San Francisco Stata Conference Chicago 2011 The views presented here are my own and do not necessarily represent the
More informationHow To Understand The Network Of A Network
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 informationMining Social-Network Graphs
342 Chapter 10 Mining Social-Network Graphs There is much information to be gained by analyzing the large-scale data that is derived from social networks. The best-known example of a social network is
More informationDATA ANALYSIS II. Matrix Algorithms
DATA ANALYSIS II Matrix Algorithms Similarity Matrix Given a dataset D = {x i }, i=1,..,n consisting of n points in R d, let A denote the n n symmetric similarity matrix between the points, given as where
More informationTHE ROLE OF SOCIOGRAMS IN SOCIAL NETWORK ANALYSIS. Maryann Durland Ph.D. EERS Conference 2012 Monday April 20, 10:30-12:00
THE ROLE OF SOCIOGRAMS IN SOCIAL NETWORK ANALYSIS Maryann Durland Ph.D. EERS Conference 2012 Monday April 20, 10:30-12:00 FORMAT OF PRESENTATION Part I SNA overview 10 minutes Part II Sociograms Example
More informationBayesian Nash Equilibrium
. Bayesian Nash Equilibrium . In the final two weeks: Goals Understand what a game of incomplete information (Bayesian game) is Understand how to model static Bayesian games Be able to apply Bayes Nash
More informationSocial Network Analysis Measuring, Mapping, and Modeling Collections of Connections
C H A P T E R 3 Social Network Analysis Measuring, Mapping, and Modeling Collections of Connections O U T L I N E 3.1 Introduction 31 3.2 The Network Perspective 32 3.2.1 A Simple Twitter Network Example
More informationNetwork VisualizationS
Network VisualizationS When do they make sense? Where to start? Clement Levallois, Assist. Prof. EMLYON Business School v. 1.1, January 2014 Bio notes Education in economics, management, history of science
More informationMining Social Network Graphs
Mining Social Network Graphs Debapriyo Majumdar Data Mining Fall 2014 Indian Statistical Institute Kolkata November 13, 17, 2014 Social Network No introduc+on required Really? We s7ll need to understand
More informationSix Degrees: The Science of a Connected Age. Duncan Watts Columbia University
Six Degrees: The Science of a Connected Age Duncan Watts Columbia University Outline The Small-World Problem What is a Science of Networks? Why does it matter? Six Degrees Six degrees of separation between
More informationOpen Source Software Developer and Project Networks
Open Source Software Developer and Project Networks Matthew Van Antwerp and Greg Madey University of Notre Dame {mvanantw,gmadey}@cse.nd.edu Abstract. This paper outlines complex network concepts and how
More informationHandout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010. Chapter 7: Digraphs
MCS-236: Graph Theory Handout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010 Chapter 7: Digraphs Strong Digraphs Definitions. A digraph is an ordered pair (V, E), where V is the set
More informationFinancial network analysis
Presentation at Payments System Oversight Course Central Bank of Bolivia, i La Paz 25 November 2011 Financial network analysis Kimmo Soramäki kimmo@soramaki.net, www.fna.fi fi Growing interest in networks
More informationGraph Theory and Complex Networks: An Introduction. Chapter 08: Computer networks
Graph Theory and Complex Networks: An Introduction Maarten van Steen VU Amsterdam, Dept. Computer Science Room R4.20, steen@cs.vu.nl Chapter 08: Computer networks Version: March 3, 2011 2 / 53 Contents
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 informationStrength of Weak Ties, Structural Holes, Closure and Small Worlds. Steve Borgatti MGT 780, Spring 2010 LINKS Center, U of Kentucky
Strength of Weak Ties, Structural Holes, Closure and Small Worlds Steve orgatti MGT 780, Spring 2010 LINKS Center, U of Kentucky Strength of Weak Ties theory Granovetter 1973 Overall idea Weak ties are
More informationLecture 16 : Relations and Functions DRAFT
CS/Math 240: Introduction to Discrete Mathematics 3/29/2011 Lecture 16 : Relations and Functions Instructor: Dieter van Melkebeek Scribe: Dalibor Zelený DRAFT In Lecture 3, we described a correspondence
More informationSCAN: A Structural Clustering Algorithm for Networks
SCAN: A Structural Clustering Algorithm for Networks Xiaowei Xu, Nurcan Yuruk, Zhidan Feng (University of Arkansas at Little Rock) Thomas A. J. Schweiger (Acxiom Corporation) Networks scaling: #edges connected
More informationMetabolic Network Analysis
Metabolic Network nalysis Overview -- modelling chemical reaction networks -- Levels of modelling Lecture II: Modelling chemical reaction networks dr. Sander Hille shille@math.leidenuniv.nl http://www.math.leidenuniv.nl/~shille
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 informationA comparative study of social network analysis tools
Membre de Membre de A comparative study of social network analysis tools David Combe, Christine Largeron, Előd Egyed-Zsigmond and Mathias Géry International Workshop on Web Intelligence and Virtual Enterprises
More informationGraph Mining Techniques for Social Media Analysis
Graph Mining Techniques for Social Media Analysis Mary McGlohon Christos Faloutsos 1 1-1 What is graph mining? Extracting useful knowledge (patterns, outliers, etc.) from structured data that can be represented
More informationChapter 29 Scale-Free Network Topologies with Clustering Similar to Online Social Networks
Chapter 29 Scale-Free Network Topologies with Clustering Similar to Online Social Networks Imre Varga Abstract In this paper I propose a novel method to model real online social networks where the growing
More informationSocial Networks and Social Media
Social Networks and Social Media Social Media: Many-to-Many Social Networking Content Sharing Social Media Blogs Microblogging Wiki Forum 2 Characteristics of Social Media Consumers become Producers Rich
More informationAnalyzing Enterprise Social Media Networks
Analyzing Enterprise Social Media Networks Marc Smith, Derek L. Hansen, Eric Gleave Abstract Broadening adoption of social media applications within the enterprise offers a new and valuable data source
More informationIntroduction to social network analysis
Introduction to social network analysis Paola Tubaro University of Greenwich, London 26 March 2012 Introduction to social network analysis Introduction Introducing SNA Rise of online social networking
More informationSocial Prediction in Mobile Networks: Can we infer users emotions and social ties?
Social Prediction in Mobile Networks: Can we infer users emotions and social ties? Jie Tang Tsinghua University, China 1 Collaborate with John Hopcroft, Jon Kleinberg (Cornell) Jinghai Rao (Nokia), Jimeng
More informationGraph Theory and Complex Networks: An Introduction. Chapter 06: Network analysis. Contents. Introduction. Maarten van Steen. Version: April 28, 2014
Graph Theory and Complex Networks: An Introduction Maarten van Steen VU Amsterdam, Dept. Computer Science Room R.0, steen@cs.vu.nl Chapter 0: Version: April 8, 0 / Contents Chapter Description 0: Introduction
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 informationDESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.
DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize,
More informationA Non-Linear Schema Theorem for Genetic Algorithms
A Non-Linear Schema Theorem for Genetic Algorithms William A Greene Computer Science Department University of New Orleans New Orleans, LA 70148 bill@csunoedu 504-280-6755 Abstract We generalize Holland
More informationUSING SPECTRAL RADIUS RATIO FOR NODE DEGREE TO ANALYZE THE EVOLUTION OF SCALE- FREE NETWORKS AND SMALL-WORLD NETWORKS
USING SPECTRAL RADIUS RATIO FOR NODE DEGREE TO ANALYZE THE EVOLUTION OF SCALE- FREE NETWORKS AND SMALL-WORLD NETWORKS Natarajan Meghanathan Jackson State University, 1400 Lynch St, Jackson, MS, USA natarajan.meghanathan@jsums.edu
More informationEquivalence Concepts for Social Networks
Equivalence Concepts for Social Networks Tom A.B. Snijders University of Oxford March 26, 2009 c Tom A.B. Snijders (University of Oxford) Equivalences in networks March 26, 2009 1 / 40 Outline Structural
More informationUnderstanding Sociograms
Understanding Sociograms A Guide to Understanding Network Analysis Mapping Developed for Clients of: Durland Consulting, Inc. Elburn, IL Durland Consulting, Inc. Elburn IL Copyright 2003 Durland Consulting,
More informationWhat is Network Mapping?
Network Mapping Module #8: Systems Change Methods What is Network Mapping? Is a process for visualizing and interpreting connections within a group Can strengthen the effectiveness of the group Can help
More informationCourse on Social Network Analysis Graphs and Networks
Course on Social Network Analysis Graphs and Networks Vladimir Batagelj University of Ljubljana Slovenia V. Batagelj: Social Network Analysis / Graphs and Networks 1 Outline 1 Graph...............................
More informationSome questions... Graphs
Uni Innsbruck Informatik - 1 Uni Innsbruck Informatik - 2 Some questions... Peer-to to-peer Systems Analysis of unstructured P2P systems How scalable is Gnutella? How robust is Gnutella? Why does FreeNet
More informationECO 199 B GAMES OF STRATEGY Spring Term 2004 PROBLEM SET 4 B DRAFT ANSWER KEY 100-3 90-99 21 80-89 14 70-79 4 0-69 11
The distribution of grades was as follows. ECO 199 B GAMES OF STRATEGY Spring Term 2004 PROBLEM SET 4 B DRAFT ANSWER KEY Range Numbers 100-3 90-99 21 80-89 14 70-79 4 0-69 11 Question 1: 30 points Games
More informationChapter 3. Strong and Weak Ties
From the book Networks, Crowds, and Markets: Reasoning about a Highly Connected orld. By David Easley and Jon Kleinberg. Cambridge University Press, 2010. Complete preprint on-line at http://www.cs.cornell.edu/home/kleinber/networks-book/
More informationGeneral Network Analysis: Graph-theoretic. COMP572 Fall 2009
General Network Analysis: Graph-theoretic 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 informationData Mining on Social Networks. Dionysios Sotiropoulos Ph.D.
Data Mining on Social Networks Dionysios Sotiropoulos Ph.D. 1 Contents What are Social Media? Mathematical Representation of Social Networks Fundamental Data Mining Concepts Data Mining Tasks on Digital
More informationAlgebra 2 Chapter 1 Vocabulary. identity - A statement that equates two equivalent expressions.
Chapter 1 Vocabulary identity - A statement that equates two equivalent expressions. verbal model- A word equation that represents a real-life problem. algebraic expression - An expression with variables.
More informationSocial Influence Analysis in Social Networking Big Data: Opportunities and Challenges. Presenter: Sancheng Peng Zhaoqing University
Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges Presenter: Sancheng Peng Zhaoqing University 1 2 3 4 35 46 7 Contents Introduction Relationship between SIA and BD
More informationDegree distribution in random Apollonian networks structures
Degree distribution in random Apollonian networks structures Alexis Darrasse joint work with Michèle Soria ALÉA 2007 Plan 1 Introduction 2 Properties of real-life graphs Distinctive properties Existing
More informationINFORMATION from a single node (entity) can reach other
1 Network Infusion to Infer Information Sources in Networks Soheil Feizi, Muriel Médard, Gerald Quon, Manolis Kellis, and Ken Duffy arxiv:166.7383v1 [cs.si] 23 Jun 216 Abstract Several significant models
More informationA Brief Survey on Anonymization Techniques for Privacy Preserving Publishing of Social Network Data
A Brief Survey on Anonymization Techniques for Privacy Preserving Publishing of Social Network Data Bin Zhou School of Computing Science Simon Fraser University, Canada bzhou@cs.sfu.ca Jian Pei School
More informationSocial network analysis with R sna package
Social network analysis with R sna package George Zhang iresearch Consulting Group (China) bird@iresearch.com.cn birdzhangxiang@gmail.com Social network (graph) definition G = (V,E) Max edges = N All possible
More informationNetwork Analytics in Marketing
Network Analytics in Marketing Prof. Dr. Daning Hu Department of Informatics University of Zurich Nov 13th, 2014 Introduction: Network Analytics in Marketing Marketing channels and business networks have
More informationSocial and Economic Networks: Lecture 1, Networks?
Social and Economic Networks: Lecture 1, Networks? Alper Duman Izmir University Economics, February 26, 2013 Conventional economics assume that all agents are either completely connected or totally isolated.
More informationNetwork Analysis: Lecture 1. Sacha Epskamp 02-09-2014
: Lecture 1 University of Amsterdam Department of Psychological Methods 02-09-2014 Who are you? What is your specialization? Why are you here? Are you familiar with the network perspective? How familiar
More informationTemporal Dynamics of Scale-Free Networks
Temporal Dynamics of Scale-Free Networks Erez Shmueli, Yaniv Altshuler, and Alex Sandy Pentland MIT Media Lab {shmueli,yanival,sandy}@media.mit.edu Abstract. Many social, biological, and technological
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 informationExamining graduate committee faculty compositions- A social network analysis example. Kathryn Shirley and Kelly D. Bradley. University of Kentucky
Examining graduate committee faculty compositions- A social network analysis example Kathryn Shirley and Kelly D. Bradley University of Kentucky Graduate committee social network analysis 1 Abstract Social
More informationGraph/Network Visualization
Graph/Network Visualization Data model: graph structures (relations, knowledge) and networks. Applications: Telecommunication systems, Internet and WWW, Retailers distribution networks knowledge representation
More information! E6893 Big Data Analytics Lecture 10:! Linked Big Data Graph Computing (II)
E6893 Big Data Analytics Lecture 10: Linked Big Data Graph Computing (II) Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science Mgr., Dept. of Network Science and
More informationGreedy Routing on Hidden Metric Spaces as a Foundation of Scalable Routing Architectures
Greedy Routing on Hidden Metric Spaces as a Foundation of Scalable Routing Architectures Dmitri Krioukov, kc claffy, and Kevin Fall CAIDA/UCSD, and Intel Research, Berkeley Problem High-level Routing is
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 informationAttacking Anonymized Social Network
Attacking Anonymized Social Network From: Wherefore Art Thou RX3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography Presented By: Machigar Ongtang (Ongtang@cse.psu.edu ) Social
More informationAN ANALYSIS OF A WAR-LIKE CARD GAME. Introduction
AN ANALYSIS OF A WAR-LIKE CARD GAME BORIS ALEXEEV AND JACOB TSIMERMAN Abstract. In his book Mathematical Mind-Benders, Peter Winkler poses the following open problem, originally due to the first author:
More informationMarkov random fields and Gibbs measures
Chapter Markov random fields and Gibbs measures 1. Conditional independence Suppose X i is a random element of (X i, B i ), for i = 1, 2, 3, with all X i defined on the same probability space (.F, P).
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 informationTie Visualization in NodeXL
Tie Visualization in NodeXL Nick Gramsky ngramsky at cs.umd.edu CMSC 838C Social Computing University of Maryland College Park Abstract: The ability to visualize a network as it varies over time has become
More information1 Introduction. Dr. T. Srinivas Department of Mathematics Kakatiya University Warangal 506009, AP, INDIA tsrinivasku@gmail.com
A New Allgoriitthm for Miiniimum Costt Liinkiing M. Sreenivas Alluri Institute of Management Sciences Hanamkonda 506001, AP, INDIA allurimaster@gmail.com Dr. T. Srinivas Department of Mathematics Kakatiya
More informationSOCIAL NETWORK ANALYSIS EVALUATING THE CUSTOMER S INFLUENCE FACTOR OVER BUSINESS EVENTS
SOCIAL NETWORK ANALYSIS EVALUATING THE CUSTOMER S INFLUENCE FACTOR OVER BUSINESS EVENTS Carlos Andre Reis Pinheiro 1 and Markus Helfert 2 1 School of Computing, Dublin City University, Dublin, Ireland
More informationCost effective Outbreak Detection in Networks
Cost effective Outbreak Detection in Networks Jure Leskovec Joint work with Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, and Natalie Glance Diffusion in Social Networks One of
More informationTU e. Advanced Algorithms: experimentation project. The problem: load balancing with bounded look-ahead. Input: integer m 2: number of machines
The problem: load balancing with bounded look-ahead Input: integer m 2: number of machines integer k 0: the look-ahead numbers t 1,..., t n : the job sizes Problem: assign jobs to machines machine to which
More informationNodeXL for Network analysis Demo/hands-on at NICAR 2012, St Louis, Feb 24. Peter Aldhous, San Francisco Bureau Chief. peter@peteraldhous.
NodeXL for Network analysis Demo/hands-on 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 information