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Table of 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.
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.