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140327t2015 sz d|||| |||| 00| 0 eng d |
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|c 45805
|d 45805
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|a 9783319238821
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| 040 |
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|a AR-BaUFA
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| 090 |
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|a 519.8 LUK
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| 100 |
1 |
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|a Luke, Douglas A.
|9 67687
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| 245 |
0 |
0 |
|a A user`s guide to network analysis in R
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| 260 |
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|a Berna
|b Springer international publishing
|c 2015
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| 300 |
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|a 238 p.
|b grafs., tbls.
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| 490 |
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|a Use R!
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| 500 |
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|a 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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| 590 |
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|a DIFU2018D
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| 650 |
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0 |
|a ANALISIS DE REDES
|2 Agrovoc
|9 3585
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| 650 |
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0 |
|a ESTADISTICA COMO CIENCIA
|2 Agrovoc
|9 35845
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| 650 |
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0 |
|9 182
|a METODOS ESTADISTICOS
|2 Agrovoc
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| 650 |
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0 |
|a INFORMATICA
|2 Agrovoc
|9 35846
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| 830 |
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|a Use R!
|9 67688
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| 942 |
0 |
0 |
|c LIBRO14D
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| 976 |
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|a AAG
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| 992 |
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|c Modelos estadísticos
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