The Resource Statistical analysis of network data with R, Eric D. Kolaczyk, Gábor Csárdi

Statistical analysis of network data with R, Eric D. Kolaczyk, Gábor Csárdi

Label
Statistical analysis of network data with R
Title
Statistical analysis of network data with R
Statement of responsibility
Eric D. Kolaczyk, Gábor Csárdi
Creator
Contributor
Author
Subject
Genre
Language
eng
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)."
Member of
Cataloging source
GW5XE
http://library.link/vocab/creatorName
Kolaczyk, Eric D
Dewey number
003.015195
Index
index present
LC call number
QA402
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
NLM call number
Online Book
http://library.link/vocab/relatedWorkOrContributorName
Csárdi, Gábor
Series statement
Use R!
http://library.link/vocab/subjectName
  • System analysis
  • R (Computer program language)
  • Systems Analysis
  • SCIENCE
  • TECHNOLOGY & ENGINEERING
  • R (Computer program language)
  • System analysis
  • Statistisches Modell
  • Systemanalyse
  • Statistik
  • Programmiersprache
  • Statistics
  • Statistics and Computing/Statistics Programs
  • Statistical Theory and Methods
  • Complex Systems
  • Signal, Image and Speech Processing
  • Statistical Physics, Dynamical Systems and Complexity
  • Computational Biology/Bioinformatics
Label
Statistical analysis of network data with R, Eric D. Kolaczyk, Gábor Csárdi
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-1-4939-0983-4
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references (pages 197-204) and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
  • 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
  • 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
  • 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
  • 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
Dimensions
unknown
Extent
1 online resource (xiii, 207 pages).
File format
unknown
Form of item
online
Isbn
9781493909834
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-1-4939-0983-4
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)880842217
  • (OCoLC)ocn880842217
Label
Statistical analysis of network data with R, Eric D. Kolaczyk, Gábor Csárdi
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-1-4939-0983-4
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references (pages 197-204) and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
  • 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
  • 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
  • 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
  • 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
Dimensions
unknown
Extent
1 online resource (xiii, 207 pages).
File format
unknown
Form of item
online
Isbn
9781493909834
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-1-4939-0983-4
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)880842217
  • (OCoLC)ocn880842217

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