Luke A User's Guide to Network Analysis in R

1. Auflage 2015
ISBN: 978-3-319-23883-8
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 241 Seiten

Reihe: Use R!

Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.

Douglas Luke is Professor and Director of the Center for Public Health Systems Science at the George Warren Brown School of Social Work at Washington University in St. Louis. He is a leading researcher in the fields of health policy, and his work focuses on the evaluation, dissemination, and implementation of evidence-based public health policies. Dr. Luke has worked extensively with systems science methodologies, especially the analysis of social networks with regards to the implementation of public health policies. He is a member of the Institute for Public Health, a founding member of the Washington University Network of Dissemination and Implementation Researchers (WUNDIR), and serves on the Interagency Committee on Smoking and Health at the U.S. Department of Health and Human Services.

Autoren/Hrsg.

Luke, Douglas

Inhaltsverzeichnis

1;Preface;8
2;Contents;10
3;1 Introducing Network Analysis in R;14
3.1;1.1 What Are Networks?;14
3.2;1.2 What Is Network Analysis?;16
3.3;1.3 Five Good Reasons to Do Network Analysis in R;17
3.3.1;1.3.1 Scope of R;17
3.3.2;1.3.2 Free and Open Nature of R;18
3.3.3;1.3.3 Data and Project Management Capabilities of R;18
3.3.4;1.3.4 Breadth of Network Packages in R;19
3.3.5;1.3.5 Strength of Network Modeling in R;19
3.4;1.4 Scope of Book and Resources;19
3.4.1;1.4.1 Scope;19
3.4.2;1.4.2 Book Roadmap;20
3.4.3;1.4.3 Resources;21
4;Part I Network Analysis Fundamentals;22
4.1;2 The Network Analysis `Five-Number Summary';23
4.1.1;2.1 Network Analysis in R: Where to Start;23
4.1.2;2.2 Preparation;23
4.1.3;2.3 Simple Visualization;24
4.1.4;2.4 Basic Description;24
4.1.4.1;2.4.1 Size;24
4.1.4.2;2.4.2 Density;26
4.1.4.3;2.4.3 Components;27
4.1.4.4;2.4.4 Diameter;27
4.1.5;2.5 Clustering Coefficient;28
4.2;3 Network Data Management in R;29
4.2.1;3.1 Network Data Concepts;29
4.2.1.1;3.1.1 Network Data Structures;29
4.2.1.1.1;3.1.1.1 Sociomatrices;30
4.2.1.1.2;3.1.1.2 Edge-Lists;31
4.2.1.2;3.1.2 Information Stored in Network Objects;32
4.2.2;3.2 Creating and Managing Network Objects in R;33
4.2.2.1;3.2.1 Creating a Network Object in statnet;33
4.2.2.2;3.2.2 Managing Node and Tie Attributes;36
4.2.2.2.1;3.2.2.1 Node Attributes;37
4.2.2.2.2;3.2.2.2 Tie Attributes;38
4.2.2.3;3.2.3 Creating a Network Object in igraph;40
4.2.2.4;3.2.4 Going Back and Forth Between statnet and igraph;42
4.2.3;3.3 Importing Network Data;42
4.2.4;3.4 Common Network Data Tasks;44
4.2.4.1;3.4.1 Filtering Networks Based on Vertex or Edge AttributeValues;44
4.2.4.1.1;3.4.1.1 Filtering Based on Node Values;44
4.2.4.1.2;3.4.1.2 Removing Isolates;46
4.2.4.1.3;3.4.1.3 Filtering Based on Edge Values;47
4.2.4.2;3.4.2 Transforming a Directed Network to a Non-directedNetwork;51
5;Part II Visualization;54
5.1;4 Basic Network Plotting and Layout;55
5.1.1;4.1 The Challenge of Network Visualization;55
5.1.2;4.2 The Aesthetics of Network Layouts;57
5.1.3;4.3 Basic Plotting Algorithms and Methods;59
5.1.3.1;4.3.1 Finer Control Over Network Layout;60
5.1.3.2;4.3.2 Network Graph Layouts Using igraph;62
5.2;5 Effective Network Graphic Design;64
5.2.1;5.1 Basic Principles;64
5.2.2;5.2 Design Elements;64
5.2.2.1;5.2.1 Node Color;65
5.2.2.2;5.2.2 Node Shape;69
5.2.2.3;5.2.3 Node Size;71
5.2.2.4;5.2.4 Node Label;75
5.2.2.5;5.2.5 Edge Width;77
5.2.2.6;5.2.6 Edge Color;78
5.2.2.7;5.2.7 Edge Type;79
5.2.2.8;5.2.8 Legends;80
5.3;6 Advanced Network Graphics;82
5.3.1;6.1 Interactive Network Graphics;82
5.3.1.1;6.1.1 Simple Interactive Networks in igraph;83
5.3.1.2;6.1.2 Publishing Web-Based Interactive Network Diagrams;83
5.3.1.3;6.1.3 Statnet Web: Interactive statnet with shiny;86
5.3.2;6.2 Specialized Network Diagrams;86
5.3.2.1;6.2.1 Arc Diagrams;87
5.3.2.2;6.2.2 Chord Diagrams;88
5.3.2.3;6.2.3 Heatmaps for Network Data;91
5.3.3;6.3 Creating Network Diagrams with Other R Packages;93
5.3.3.1;6.3.1 Network Diagrams with ggplot2;93
6;Part III Description and Analysis;97
6.1;7 Actor Prominence;98
6.1.1;7.1 Introduction;98
6.1.2;7.2 Centrality: Prominence for Undirected Networks;99
6.1.2.1;7.2.1 Three Common Measures of Centrality;100
6.1.2.1.1;7.2.1.1 Degree Centrality;100
6.1.2.1.2;7.2.1.2 Closeness Centrality;101
6.1.2.1.3;7.2.1.3 Betweenness Centrality;101
6.1.2.2;7.2.2 Centrality Measures in R;102
6.1.2.3;7.2.3 Centralization: Network Level Indices of Centrality;103
6.1.2.4;7.2.4 Reporting Centrality;104
6.1.3;7.3 Cutpoints and Bridges;108
6.2;8 Subgroups;112
6.2.1;8.1 Introduction;112
6.2.2;8.2 Social Cohesion;113
6.2.2.1;8.2.1 Cliques;114
6.2.2.2;8.2.2 k-Cores;117
6.2.3;8.3 Community Detection;122
6.2.3.1;8.3.1 Modularity;122
6.2.3.2;8.3.2 Community Detection Algorithms;125
6.3;9 Affiliation Networks;131
6.3.1;9.1 Defining Affiliation Networks;131
6.3.1.1;9.1.1 Affiliations as 2-Mode Networks;132
6.3.1.2;9.1.2 Bipartite Graphs;132
6.3.2;9.2 Affiliation Network Basics;133
6.3.2.1;9.2.1 Creating Affiliation Networks from Incidence Matrices;133
6.3.2.2;9.2.2 Creating Affiliation Networks from Edge Lists;135
6.3.2.3;9.2.3 Plotting Affiliation Networks;136
6.3.2.4;9.2.4 Projections;137
6.3.3;9.3 Example: Hollywood Actors as an Affiliation Network;139
6.3.3.1;9.3.1 Analysis of Entire Hollywood Affiliation Network;140
6.3.3.2;9.3.2 Analysis of the Actor and Movie Projections;145
7;Part IV Modeling;151
7.1;10 Random Network Models;152
7.1.1;10.1 The Role of Network Models;152
7.1.2;10.2 Models of Network Structure and Formation;153
7.1.2.1;10.2.1 Erd?s-Rényi Random Graph Model;153
7.1.2.2;10.2.2 Small-World Model;156
7.1.2.3;10.2.3 Scale-Free Models;159
7.1.3;10.3 Comparing Random Models to Empirical Networks;165
7.2;11 Statistical Network Models;168
7.2.1;11.1 Introduction;168
7.2.2;11.2 Building Exponential Random Graph Models;170
7.2.2.1;11.2.1 Building a Null Model;172
7.2.2.2;11.2.2 Including Node Attributes;174
7.2.2.3;11.2.3 Including Dyadic Predictors;176
7.2.2.4;11.2.4 Including Relational Terms (Network Predictors);180
7.2.2.5;11.2.5 Including Local Structural Predictors (Dyad Dependency);182
7.2.3;11.3 Examining Exponential Random Graph Models;184
7.2.3.1;11.3.1 Model Interpretation;184
7.2.3.2;11.3.2 Model Fit;185
7.2.3.3;11.3.3 Model Diagnostics;188
7.2.3.4;11.3.4 Simulating Networks Based on Fit Model;188
7.3;12 Dynamic Network Models;193
7.3.1;12.1 Introduction;193
7.3.1.1;12.1.1 Dynamic Networks;193
7.3.1.2;12.1.2 RSiena;195
7.3.2;12.2 Data Preparation;196
7.3.3;12.3 Model Specification and Estimation;202
7.3.3.1;12.3.1 Specification of Model Effects;202
7.3.3.2;12.3.2 Model Estimation;207
7.3.4;12.4 Model Exploration;207
7.3.4.1;12.4.1 Model Interpretation;207
7.3.4.2;12.4.2 Goodness-of-Fit;213
7.3.4.3;12.4.3 Model Simulations;216
7.4;13 Simulations;220
7.4.1;13.1 Simulations of Network Dynamics;220
7.4.1.1;13.1.1 Simulating Social Selection;221
7.4.1.1.1;13.1.1.1 Setting Up the Simulation;221
7.4.1.1.2;13.1.1.2 Creating an Update Function;222
7.4.1.1.3;13.1.1.3 Building a Simple Simulation of Social Selection;228
7.4.1.1.4;13.1.1.4 Interpreting the Results of the Simulation;230
7.4.1.2;13.1.2 Simulating Social Influence;231
7.4.1.2.1;13.1.2.1 Setting Up the Simulation;231
7.4.1.2.2;13.1.2.2 Creating an Update Function;232
7.4.1.2.3;13.1.2.3 Building the Simulation of Social Influence;234
7.4.1.2.4;13.1.2.4 Interpreting the Results of the Simulation;235
8;References;238