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A User_s Guide to Network Analysis in R.pdf

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    Preface
    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
    3.1.1.1 Sociomatrices
    3.1.1.2 Edge-Lists
    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
    3.2.2.1 Node Attributes
    3.2.2.2 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 AttributeValues
    3.4.1.1 Filtering Based on Node Values
    3.4.1.2 Removing Isolates
    3.4.1.3 Filtering Based on Edge Values
    3.4.2 Transforming a Directed Network to a Non-directedNetwork
    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 Edge Width
    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
    7.2.1.1 Degree Centrality
    7.2.1.2 Closeness Centrality
    7.2.1.3 Betweenness 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 and Movie 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 Erdős-Rényi 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
    13.1.1.1 Setting Up the Simulation
    13.1.1.2 Creating an Update Function
    13.1.1.3 Building a Simple Simulation of Social Selection
    13.1.1.4 Interpreting the Results of the Simulation
    13.1.2 Simulating Social Influence
    13.1.2.1 Setting Up the Simulation
    13.1.2.2 Creating an Update Function
    13.1.2.3 Building the Simulation of Social Influence
    13.1.2.4 Interpreting the Results of the Simulation
    References
    UseR! Douglas A. Luke A User’s Guide to Network Analysis in R
    Use R! Series Editors: Robert Gentleman Kurt Hornik Giovanni Parmigiani More information about this series at http://www.springer.com/series/6991
    Use R! Albert: Bayesian Computation with R (2nd ed. 2009) Bivand/Pebesma/G´omez-Rubio: Applied Spatial Data Analysis with R (2nd ed. 2013) Cook/Swayne: Interactive and Dynamic Graphics for Data Analysis: With R and GGobi Hahne/Huber/Gentleman/Falcon: Bioconductor Case Studies Paradis: Analysis of Phylogenetics and Evolution with R (2nd ed. 2012) Pfaff: Analysis of Integrated and Cointegrated Time Series with R (2nd ed. 2008) Sarkar: Lattice: Multivariate Data Visualization with R Spector: Data Manipulation with R
    Douglas A. Luke A User’s Guide to Network Analysis in R 123
    Douglas A. Luke Center for Public Health Systems Science George Warren Brown School of Social Work Washington University St. Louis, MO, USA ISSN 2197-5736 Use R! ISBN 978-3-319-23882-1 DOI 10.1007/978-3-319-23883-8 ISSN 2197-5744 (electronic) ISBN 978-3-319-23883-8 (eBook) Library of Congress Control Number: 2015955739 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com)
    To my most important social network—Sue, Alina, and Andrew
    Preface In early 2000, Stephen Hawking said that “. . .the next century will be the century of complexity.” If his prediction is true, the implication is that we will need new scien- tific theories, data collection methods, and analytic techniques that are appropriate for the study of complex systems and behavior. Network science is one such ap- proach that views the world through a network lens, where physical and social sys- tems are made up of heterogeneous actors who are connected to one another through different types of relational ties. Network analysis is the set of analytic tools used to study these types of systems. Over the past several decades network analysis has become an increasingly important part of the analytic toolbox for social, health, and physical scientists. Until recently, network analysis required specialized software, both for network data management and analyses. However, starting around 2000, network analytic tools became available in the R statistical programming environment. This not only made network analytic techniques more visible to the broader statistical community but also provided the breadth and power of R’s data management, graphic visualiza- tion, and general statistical modeling capabilities to the network analyst community. As the title suggests, this book is a user’s guide to network analysis in R. It pro- vides a practical hands-on tour of the major network analytic tasks that can currently be done in R. The book concentrates on four primary tasks that a network analyst typically concerns herself with: network data management, network visualization, network description, and network modeling. The book includes all the R code that is used in the network analysis examples. It also comes with a set of network datasets that are used throughout the book. (See Chap. 1 for more details on the structure of the book, as well as instructions on how to obtain the network data.) The book is written for anybody who has an interest in doing network analysis in R. It can be used as a secondary text in a network science or analysis class or can simply serve as a reference for network techniques in R. This book would not exist without the help, support, guidance, and mentoring I have received over the last 30 years from my own personal and professional so- cial networks. In the mid-1980s I took a graduate network analysis class from Stan Wasserman at the University of Illinois in Champaign. I remember being excited vii
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