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The Resource A User{u2019}s Guide to Network Analysis in R

A User{u2019}s Guide to Network Analysis in R

Label
A User{u2019}s Guide to Network Analysis in R
Title
A User{u2019}s Guide to Network Analysis in R
Creator
Subject
Language
eng
Member of
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Use R!
A User{u2019}s Guide to Network Analysis in R
Label
A User{u2019}s Guide to Network Analysis in R
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4199226
Publication
Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface -- Contents -- 1 Introducing Network Analysis in R -- 1.1 What Are Networks? -- 1.2 What Is Network Analysis? -- 1.3 Five Good Reasons to Do Network Analysis in R -- 1.3.1 Scope of R -- 1.3.2 Free and Open Nature of R -- 1.3.3 Data and Project Management Capabilities of R -- 1.3.4 Breadth of Network Packages in R -- 1.3.5 Strength of Network Modeling in R -- 1.4 Scope of Book and Resources -- 1.4.1 Scope -- 1.4.2 Book Roadmap -- 1.4.3 Resources -- Part I Network Analysis Fundamentals -- 2 The Network Analysis F̀ive-Number Summary' -- 2.1 Network Analysis in R: Where to Start -- 2.2 Preparation -- 2.3 Simple Visualization -- 2.4 Basic Description -- 2.4.1 Size -- 2.4.2 Density -- 2.4.3 Components -- 2.4.4 Diameter -- 2.5 Clustering Coefficient -- 3 Network Data Management in R -- 3.1 Network Data Concepts -- 3.1.1 Network Data Structures -- 3.1.1.1 Sociomatrices -- 3.1.1.2 Edge-Lists -- 3.1.2 Information Stored in Network Objects -- 3.2 Creating and Managing Network Objects in R -- 3.2.1 Creating a Network Object in statnet -- 3.2.2 Managing Node and Tie Attributes -- 3.2.2.1 Node Attributes -- 3.2.2.2 Tie Attributes -- 3.2.3 Creating a Network Object in igraph -- 3.2.4 Going Back and Forth Between statnet and igraph -- 3.3 Importing Network Data -- 3.4 Common Network Data Tasks -- 3.4.1 Filtering Networks Based on Vertex or Edge AttributeValues -- 3.4.1.1 Filtering Based on Node Values -- 3.4.1.2 Removing Isolates -- 3.4.1.3 Filtering Based on Edge Values -- 3.4.2 Transforming a Directed Network to a Non-directedNetwork -- Part II Visualization -- 4 Basic Network Plotting and Layout -- 4.1 The Challenge of Network Visualization -- 4.2 The Aesthetics of Network Layouts -- 4.3 Basic Plotting Algorithms and Methods -- 4.3.1 Finer Control Over Network Layout -- 4.3.2 Network Graph Layouts Using igraph -- 5 Effective Network Graphic Design
  • 5.1 Basic Principles -- 5.2 Design Elements -- 5.2.1 Node Color -- 5.2.2 Node Shape -- 5.2.3 Node Size -- 5.2.4 Node Label -- 5.2.5 Edge Width -- 5.2.6 Edge Color -- 5.2.7 Edge Type -- 5.2.8 Legends -- 6 Advanced Network Graphics -- 6.1 Interactive Network Graphics -- 6.1.1 Simple Interactive Networks in igraph -- 6.1.2 Publishing Web-Based Interactive Network Diagrams -- 6.1.3 Statnet Web: Interactive statnet with shiny -- 6.2 Specialized Network Diagrams -- 6.2.1 Arc Diagrams -- 6.2.2 Chord Diagrams -- 6.2.3 Heatmaps for Network Data -- 6.3 Creating Network Diagrams with Other R Packages -- 6.3.1 Network Diagrams with ggplot2 -- Part III Description and Analysis -- 7 Actor Prominence -- 7.1 Introduction -- 7.2 Centrality: Prominence for Undirected Networks -- 7.2.1 Three Common Measures of Centrality -- 7.2.1.1 Degree Centrality -- 7.2.1.2 Closeness Centrality -- 7.2.1.3 Betweenness Centrality -- 7.2.2 Centrality Measures in R -- 7.2.3 Centralization: Network Level Indices of Centrality -- 7.2.4 Reporting Centrality -- 7.3 Cutpoints and Bridges -- 8 Subgroups -- 8.1 Introduction -- 8.2 Social Cohesion -- 8.2.1 Cliques -- 8.2.2 k-Cores -- 8.3 Community Detection -- 8.3.1 Modularity -- 8.3.2 Community Detection Algorithms -- 9 Affiliation Networks -- 9.1 Defining Affiliation Networks -- 9.1.1 Affiliations as 2-Mode Networks -- 9.1.2 Bipartite Graphs -- 9.2 Affiliation Network Basics -- 9.2.1 Creating Affiliation Networks from Incidence Matrices -- 9.2.2 Creating Affiliation Networks from Edge Lists -- 9.2.3 Plotting Affiliation Networks -- 9.2.4 Projections -- 9.3 Example: Hollywood Actors as an Affiliation Network -- 9.3.1 Analysis of Entire Hollywood Affiliation Network -- 9.3.2 Analysis of the Actor and Movie Projections -- Part IV Modeling -- 10 Random Network Models -- 10.1 The Role of Network Models
  • 10.2 Models of Network Structure and Formation -- 10.2.1 Erdős-Rényi Random Graph Model -- 10.2.2 Small-World Model -- 10.2.3 Scale-Free Models -- 10.3 Comparing Random Models to Empirical Networks -- 11 Statistical Network Models -- 11.1 Introduction -- 11.2 Building Exponential Random Graph Models -- 11.2.1 Building a Null Model -- 11.2.2 Including Node Attributes -- 11.2.3 Including Dyadic Predictors -- 11.2.4 Including Relational Terms (Network Predictors) -- 11.2.5 Including Local Structural Predictors (Dyad Dependency) -- 11.3 Examining Exponential Random Graph Models -- 11.3.1 Model Interpretation -- 11.3.2 Model Fit -- 11.3.3 Model Diagnostics -- 11.3.4 Simulating Networks Based on Fit Model -- 12 Dynamic Network Models -- 12.1 Introduction -- 12.1.1 Dynamic Networks -- 12.1.2 RSiena -- 12.2 Data Preparation -- 12.3 Model Specification and Estimation -- 12.3.1 Specification of Model Effects -- 12.3.2 Model Estimation -- 12.4 Model Exploration -- 12.4.1 Model Interpretation -- 12.4.2 Goodness-of-Fit -- 12.4.3 Model Simulations -- 13 Simulations -- 13.1 Simulations of Network Dynamics -- 13.1.1 Simulating Social Selection -- 13.1.1.1 Setting Up the Simulation -- 13.1.1.2 Creating an Update Function -- 13.1.1.3 Building a Simple Simulation of Social Selection -- 13.1.1.4 Interpreting the Results of the Simulation -- 13.1.2 Simulating Social Influence -- 13.1.2.1 Setting Up the Simulation -- 13.1.2.2 Creating an Update Function -- 13.1.2.3 Building the Simulation of Social Influence -- 13.1.2.4 Interpreting the Results of the Simulation -- References
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Dimensions
unknown
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b4384608'}
Extent
1 online resource (241 pages)
Form of item
online
Isbn
9783319238838
Media category
computer
Media MARC source
rdamedia
Media type code
c
Sound
unknown sound
Specific material designation
remote

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