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A user`s guide to network analysis in R

Ubicación: 519.8 LUK
Por: Luke, Douglas A.
Series Use R!.Publicación: Berna : Springer international publishing, 2015Descripción: 238 p. grafs., tbls.ISBN: 9783319238821.Tipo de material: Libro de préstamo de 14 días.Tema(s): ANALISIS DE REDES | ESTADISTICA COMO CIENCIA | METODOS ESTADISTICOS | INFORMATICA
Lista(s) en las que aparece este ítem: compra 2018 agosto
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Libro de préstamo de 14 días Libro de préstamo de 14 días Biblioteca Central

Facultad de Agronomía - Universidad de Buenos Aires

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519.8 LUK (Navegar estantería) Disponible

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 ‘Five-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
3.1.2 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
3.2.3 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 Attribute
Values
3.4.2 Transforming a Directed Network to a Non-directed
Network
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 EdgeWidth
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
7.2.2 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 andMovie 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 Erd˝os-R´enyi 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
13.1.2 Simulating Social Influence
References

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