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E-raamat: User's Guide to Network Analysis in R

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  • Formaat: PDF+DRM
  • Sari: Use R!
  • Ilmumisaeg: 14-Dec-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319238838
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User's Guide to Network Analysis in RSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Use R!
  • Ilmumisaeg: 14-Dec-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319238838
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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.

Introducing Network Analysis in R.- The Network Analysis "5 Number Summary".- Network Data Management in R.- Basic Network Plotting and Layout.- Effective Network Graphic Design.- Advanced Network Graphics.- Actor Prominence.- Subgroups.- Affiliation Networks.- Random Network Models.- Statistical Network Models.- Dynamic Network Models.- Simulations.
1 Introducing Network Analysis in R
1(10)
1.1 What Are Networks?
1(2)
1.2 What Is Network Analysis?
3(1)
1.3 Five Good Reasons to Do Network Analysis in R
4(2)
1.3.1 Scope of R
4(1)
1.3.2 Free and Open Nature of R
5(1)
1.3.3 Data and Project Management Capabilities of R
5(1)
1.3.4 Breadth of Network Packages in R
6(1)
1.3.5 Strength of Network Modeling in R
6(1)
1.4 Scope of Book and Resources
6(5)
1.4.1 Scope
6(1)
1.4.2 Book Roadmap
7(1)
1.4.3 Resources
8(3)
Part I Network Analysis Fundamentals
2 The Network Analysis `Five-Number Summary'
11(6)
2.1 Network Analysis in R: Where to Start
11(1)
2.2 Preparation
11(1)
2.3 Simple Visualization
12(1)
2.4 Basic Description
12(4)
2.4.1 Size
12(2)
2.4.2 Density
14(1)
2.4.3 Components
15(1)
2.4.4 Diameter
15(1)
2.5 Clustering Coefficient
16(1)
3 Network Data Management in R
17(28)
3.1 Network Data Concepts
17(4)
3.1.1 Network Data Structures
17(3)
3.1.2 Information Stored in Network Objects
20(1)
3.2 Creating and Managing Network Objects in R
21(9)
3.2.1 Creating a Network Object in statnet
21(3)
3.2.2 Managing Node and Tie Attributes
24(4)
3.2.3 Creating a Network Object in igraph
28(2)
3.2.4 Going Back and Forth Between statnet and igraph
30(1)
3.3 Importing Network Data
30(2)
3.4 Common Network Data Tasks
32(13)
3.4.1 Filtering Networks Based on Vertex or Edge Attribute Values
32(7)
3.4.2 Transforming a Directed Network to a Non-directed Network
39(6)
Part II Visualization
4 Basic Network Plotting and Layout
45(10)
4.1 The Challenge of Network Visualization
45(2)
4.2 The Aesthetics of Network Layouts
47(2)
4.3 Basic Plotting Algorithms and Methods
49(6)
4.3.1 Finer Control Over Network Layout
50(2)
4.3.2 Network Graph Layouts Using igraph
52(3)
5 Effective Network Graphic Design
55(18)
5.1 Basic Principles
55(1)
5.2 Design Elements
55(18)
5.2.1 Node Color
56(4)
5.2.2 Node Shape
60(2)
5.2.3 Node Size
62(4)
5.2.4 Node Label
66(2)
5.2.5 Edge Width
68(1)
5.2.6 Edge Color
69(1)
5.2.7 Edge Type
70(1)
5.2.8 Legends
71(2)
6 Advanced Network Graphics
73(18)
6.1 Interactive Network Graphics
73(4)
6.1.1 Simple Interactive Networks in igraph
74(1)
6.1.2 Publishing Web-Based Interactive Network Diagrams
74(3)
6.1.3 Statnet Web: Interactive statnet with shiny
77(1)
6.2 Specialized Network Diagrams
77(7)
6.2.1 Arc Diagrams
78(1)
6.2.2 Chord Diagrams
79(3)
6.2.3 Heatmaps for Network Data
82(2)
6.3 Creating Network Diagrams with Other R Packages
84(7)
6.3.1 Network Diagrams with ggplot2
84(7)
Part III Description and Analysis
7 Actor Prominence
91(14)
7.1 Introduction
91(1)
7.2 Centrality: Prominence for Undirected Networks
92(9)
7.2.1 Three Common Measures of Centrality
93(2)
7.2.2 Centrality Measures in R
95(1)
7.2.3 Centralization: Network Level Indices of Centrality
96(1)
7.2.4 Reporting Centrality
97(4)
7.3 Cutpoints and Bridges
101(4)
8 Subgroups
105(20)
8.1 Introduction
105(1)
8.2 Social Cohesion
106(9)
8.2.1 Cliques
107(3)
8.2.2 k-Cores
110(5)
8.3 Community Detection
115(10)
8.3.1 Modularity
115(3)
8.3.2 Community Detection Algorithms
118(7)
9 Affiliation Networks
125(22)
9.1 Defining Affiliation Networks
125(2)
9.1.1 Affiliations as 2-Mode Networks
126(1)
9.1.2 Bipartite Graphs
126(1)
9.2 Affiliation Network Basics
127(6)
9.2.1 Creating Affiliation Networks from Incidence Matrices
127(2)
9.2.2 Creating Affiliation Networks from Edge Lists
129(1)
9.2.3 Plotting Affiliation Networks
130(1)
9.2.4 Projections
131(2)
9.3 Example: Hollywood Actors as an Affiliation Network
133(14)
9.3.1 Analysis of Entire Holly wood Affiliation Network
134(5)
9.3.2 Analysis of the Actor and Movie Projections
139(8)
Part IV Modeling
10 Random Network Models
147(16)
10.1 The Role of Network Models
147(1)
10.2 Models of Network Structure and Formation
148(12)
10.2.1 Erdos-Renyi Random Graph Model
148(3)
10.2.2 Small-World Model
151(3)
10.2.3 Scale-Free Models
154(6)
10.3 Comparing Random Models to Empirical Networks
160(3)
11 Statistical Network Models
163(26)
11.1 Introduction
163(2)
11.2 Building Exponential Random Graph Models
165(14)
11.2.1 Building a Null Model
167(2)
11.2.2 Including Node Attributes
169(2)
11.2.3 Including Dyadic Predictors
171(4)
11.2.4 Including Relational Terms (Network Predictors)
175(2)
11.2.5 Including Local Structural Predictors (Dyad Dependency)
177(2)
11.3 Examining Exponential Random Graph Models
179(10)
11.3.1 Model Interpretation
179(1)
11.3.2 Model Fit
180(3)
11.3.3 Model Diagnostics
183(1)
11.3.4 Simulating Networks Based on Fit Model
183(6)
12 Dynamic Network Models
189(28)
12.1 Introduction
189(3)
12.1.1 Dynamic Networks
189(2)
12.1.2 RSiena
191(1)
12.2 Data Preparation
192(6)
12.3 Model Specification and Estimation
198(5)
12.3.1 Specification of Model Effects
198(5)
12.3.2 Model Estimation
203(1)
12.4 Model Exploration
203(14)
12.4.1 Model Interpretation
203(6)
12.4.2 Goodness-of-Fit
209(3)
12.4.3 Model Simulations
212(5)
13 Simulations
217(18)
13.1 Simulations of Network Dynamics
217(18)
13.1.1 Simulating Social Selection
218(10)
13.1.2 Simulating Social Influence
228(7)
References 235
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.
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