| Description |
1 online resource (xiii, 207 pages) |
| Series |
Use R! |
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Use R!
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| Contents |
Preface; Contents; Biographies; Chapter 1 Introduction; 1.1 Why Networks?; 1.2 Types of Network Analysis; 1.2.1 Visualizing and Characterizing Networks; 1.2.2 Network Modeling and Inference; 1.2.3 Network Processes; 1.3 Why Use R for Network Analysis?; 1.4 About This Book; 1.5 About the R code; Chapter 2 Manipulating Network Data; 2.1 Introduction; 2.2 Creating Network Graphs; 2.2.1 Undirected and Directed Graphs; 2.2.2 Representations for Graphs; 2.2.3 Operations on Graphs; 2.3 Decorating Network Graphs; 2.3.1 Vertex, Edge, and Graph Attributes; 2.3.2 Using Data Frames |
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2.4 Talking About Graphs2.4.1 Basic Graph Concepts; 2.4.2 Special Types of Graphs; 2.5 Additional Reading; Chapter 3 Visualizing Network Data; 3.1 Introduction; 3.2 Elements of Graph Visualization; 3.3 Graph Layouts; 3.4 Decorating Graph Layouts; 3.5 Visualizing Large Networks; 3.6 Using Visualization Tools Outside of R; 3.7 Additional Reading; Chapter 4 Descriptive Analysis of Network Graph Characteristics; 4.1 Introduction; 4.2 Vertex and Edge Characteristics; 4.2.1 Vertex Degree; 4.2.2 Vertex Centrality; 4.2.3 Characterizing Edges; 4.3 Characterizing Network Cohesion |
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4.3.1 Subgraphs and Censuses4.3.2 Density and Related Notions of Relative Frequency; 4.3.3 Connectivity, Cuts, and Flows; 4.4 Graph Partitioning; 4.4.1 Hierarchical Clustering; 4.4.2 Spectral Partitioning; 4.4.3 Validation of Graph Partitioning; 4.5 Assortativity and Mixing; 4.6 Additional Reading; Chapter 5 Mathematical Models for Network Graphs; 5.1 Introduction; 5.2 Classical Random Graph Models; 5.3 Generalized Random Graph Models; 5.4 Network Graph Models Based on Mechanisms; 5.4.1 Small-World Models; 5.4.2 Preferential Attachment Models |
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5.5 Assessing Significance of Network Graph Characteristics5.5.1 Assessing the Number of Communities in a Network; 5.5.2 Assessing Small World Properties; 5.6 Additional Reading; Chapter 6 Statistical Models for Network Graphs; 6.1 Introduction; 6.2 Exponential Random Graph Models; 6.2.1 General Formulation; 6.2.2 Specifying a Model; 6.2.3 Model Fitting; 6.2.4 Goodness-of-Fit; 6.3 Network Block Models; 6.3.1 Model Specification; 6.3.2 Model Fitting; 6.3.3 Goodness-of-Fit; 6.4 Latent Network Models; 6.4.1 General Formulation; 6.4.2 Specifying the Latent Effects; 6.4.3 Model Fitting |
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6.4.4 Goodness-of-Fit6.5 Additional Reading; Chapter 7 Network Topology Inference; 7.1 Introduction; 7.2 Link Prediction; 7.3 Association Network Inference; 7.3.1 Correlation Networks; 7.3.2 Partial Correlation Networks; 7.3.3 Gaussian Graphical Model Networks; 7.4 Tomographic Network Topology Inference; 7.4.1 Constraining the Problem: Tree Topologies; 7.4.2 Tomographic Inference of Tree Topologies:An Illustration; 7.5 Additional Reading; Chapter 8 Modeling and Prediction for Processes on Network Graphs; 8.1 Introduction; 8.2 Nearest Neighbor Methods; 8.3 Markov Random Fields |
| Summary |
"Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk's book Statistical Analysis of Network Data (Springer, 2009)." |
| Analysis |
statistiek |
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statistics |
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toegepaste statistiek |
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applied statistics |
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statistische analyse |
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statistical analysis |
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Statistics (General) |
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Statistiek (algemeen) |
| Bibliography |
Includes bibliographical references (pages 197-204) and index |
| Notes |
English |
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Online resource; title from PDF title page (SpringerLink, viewed June 2, 2014) |
| Subject |
System analysis -- Statistical methods
|
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R (Computer program language)
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System analysis.
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Systems Analysis
|
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systems analysis.
|
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SCIENCE -- System Theory.
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TECHNOLOGY & ENGINEERING -- Operations Research.
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System analysis
|
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R (Computer program language)
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System analysis -- Statistical methods
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Statistisches Modell
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Systemanalyse
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Statistik
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Programmiersprache
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| Form |
Electronic book
|
| Author |
Csárdi, Gábor, author
|
| ISBN |
9781493909834 |
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1493909835 |
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1493909827 |
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9781493909827 |
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9781493909841 |
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1493909843 |
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