Although longitudinal social network data are increasingly collected, there are few guides on how to navigate the range of available tools for longitudinal network analysis. The applied social scientist is left to wonder: Which model is most appropriate for my data? How should I get started with this modeling strategy? And how do I know if my model is any good? This book answers these questions. Author Scott Duxbury assumes that the reader is familiar with network measurement, description, and notation, and is versed in regression analysis, but is likely unfamiliar with statistical network methods. The goal of the book is to guide readers towards choosing, applying, assessing, and interpreting a longitudinal network model, and each chapter is organized with a specific data structure or research question in mind. A companion website includes data and R code to replicate the examples in the book.
Recenzijas
A brilliant how to for modelling dynamic network data. An exquisite balance of model intuition, assumptions and practical advice, accessible to all network / data scientists. -- Alexander John Bond This is a very timely book that provides critical skills for conducting explanatory analysis of longitudinal social network data. Both beginners, and advanced analysts can benefit from reading this book as it provides many real life examples, illustrating computational processes, interpreting results, and even furnishing R codes. For those who aspire to learn advanced topics in analyzing longitudinal social network data, this is a must-have book. -- Song Yang This book presents the state-of-art of longitudinal network analysis. It is comprehensive while staying concise, well structured, and clearly written. Definitely a moneyball in the field! -- Weihua An
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xi | |
| Acknowledgments |
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xiii | |
| About the Author |
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xv | |
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1 | (13) |
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2 | (1) |
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What This Book Does Not Cover |
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2 | (1) |
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What Are Longitudinal Network Data? |
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3 | (3) |
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6 | (2) |
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Why Not Standard Regression? |
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8 | (2) |
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10 | (3) |
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13 | (1) |
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13 | (1) |
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2 Temporal Exponential Random Graph Models |
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14 | (29) |
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What Are Network Panel Data? |
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14 | (2) |
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16 | (2) |
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Temporal Exponential Random Graph Models (TERGM) |
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18 | (1) |
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19 | (1) |
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20 | (1) |
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20 | (4) |
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Example 2.1 A TERGM Analysis of Friendship Formation Among Dutch College Students |
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Other Modeling Considerations |
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39 | (3) |
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42 | (1) |
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3 Stochastic Actor-Oriented Models |
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The Stochastic Actor-Oriented Model (SAOM) |
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43 | (2) |
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45 | (1) |
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46 | (1) |
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Example 3.1 An Actor-Oriented Analysis of Friendship Formation Among Dutch College Students |
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49 | (10) |
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Other Modeling Considerations |
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59 | (4) |
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63 | (1) |
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Selecting a Network Panel Model |
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63 | (4) |
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67 | (1) |
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4 Modeling Relational Event Data |
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68 | (24) |
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What Are Relational Event Data? |
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The Relational Event Model (REM) |
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71 | (1) |
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Example 4.1 Illegal Drug Trade on the Dark Web |
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Other Modeling Considerations |
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81 | (4) |
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85 | (2) |
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An Alternative: The Dynamic Network Actor Model |
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87 | (3) |
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90 | (1) |
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91 | (1) |
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5 Network Influence Models |
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What Do Network Influence Data Look Like? |
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93 | (1) |
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The Temporal Network Autocorrelation Model (TNAM) |
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94 | (1) |
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95 | (1) |
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96 | (1) |
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Example 5.1 A Network Influence Model of Adolescent Smoking Behavior |
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99 | (3) |
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Other Modeling Considerations |
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102 | (3) |
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Coevolution Models: SAOM for Behavioral and Network Change |
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105 | (3) |
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108 | (1) |
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109 | (1) |
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110 | (1) |
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Example 5.2 A Coevolution Influence Model of Adolescent Drinking Behavior |
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An Alternative Approach: Simulating Network Diffusion |
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118 | (1) |
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How Should We Think About Network Influence? |
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119 | (2) |
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121 | (1) |
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122 | (6) |
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122 | (1) |
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123 | (1) |
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124 | (1) |
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125 | (1) |
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Models for Relational Event Data |
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126 | (1) |
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Unobserved Heterogeneity in Network Panel Models |
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127 | (1) |
| Conclusion |
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128 | (1) |
| References |
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| Index |
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Scott Duxbury is an Assistant Professor of Sociology at the University of North Carolina at Chapel Hill. His research examines drug markets, criminal networks, quantitative and computational methods, public opinion, punishment, racism, and the criminal justice system. It has appeared in American Sociological Review, American Journal of Sociology, and Social Forces, among other outlets. Scotts book, Longitudinal Network Models, provides an introductory text to the suite of statistical models available for longitudinal network data analysis.
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