Analyzing social networks using R:
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne
SAGE
[2022]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xviii, 359 Seiten Illustrationen, Diagramme |
| ISBN: | 9781529722475 9781529722482 |
Internformat
MARC
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| 264 | 4 | |c © 2022 | |
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Datensatz im Suchindex
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| adam_text | Contents About the authors Preface Glossary of symbols Online resources 1 Introduction 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 Why networks? What are networks? Types of relations Goals of analysis Network variables as explanatory variables Network variables as outcome variables Summary Problems and exercises 2 Mathematical Foundations 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 Introduction Graphs Paths and components Adjacency matrices Ways and modes Matrix products Summary Problems and exercises 3 Research Design 3.1 Introduction 3.2 Experiments and field studies 3.3 Whole-network and personal-network research designs 3.4 Sources of network data 3.5 Types of nodes and types of ties 3.6 Actor attributes 3.7 Sampling and bounding 3.8 Sources of data reliability and validity issues 3.9 Ethical considerations 3.10 Summary 3.11 Problems and exercises xi xiii xvii xix 1 2 2 4 8 9 11 12 12 15 16 16 19 22 24 25 27 27 29 30 30 33 34 35 38 38 41 45 47 47
viil 1 CONTENTS 4 Data Collection , . 4.1 Introduction 4.2 Network questions 4.3 Question formats 4.4 Interviewee burden 4.5 Data collection and reliability 4.6 Archival data collection 4.7 Data from electronic sources 4.8 Summary 4.9 Problems and exercises 49 50 58 62 69 5 Data Management 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 Introduction The R program Data storage Importing and storing data in R Data transformation for network data Converting attributes to matrices Storing, transforming and exporting network data and results Summary Problems and exercises б Multivariate Techniques Used in Network Analysis 6.1 6.2 6.3 6.4 6.5 6.6 Introduction Multidimensional scaling Correspondence analysis Hierarchical clustering Summary Problems and exercises 7 Visualization 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 Introduction Layout Embedding node attributes Embedding tie attributes Node filtering and ego networks Closing comments Summary Problems and exercises 22 73 78 88 91 Ю0 102 ЮЗ 103 1θ7 108 108 110 114 117 117 119 120 120 132 136 140 14շ 1^2 143 8 Local Node-level Measures 8.1 Introduction ott. . 8.2 Tie composition 8.3 Valued tie composition , . 146
CONTENTS 8.4 8.5 8.6 8.7 8.8 Alter composition Ego-alter similarity Ego-network structural shape measures Summary Problems and exercises 9 Centrality 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 Introduction Basic concept Undirected, non-valued networks Directed, non-valued networks Valued networks Negative tie networks Induced centralities Summary Problems and exercises 10 Group-level Measures ļ ix 152 156 161 166 167 169 170 170 171 183 187 188 189 190 190 193 Introduction Measures based on local properties Measures based on global properties Centralization and core-peripheriness Attribute-based measures Summary Problems and exercises 194 195 201 205 207 210 211 11 Subgroups and Community Detection 213 10.1 10.2 10.3 10.4 10.5 10.6 10.7 11.1 Introduction 11.2 Cliques 11.3 Girvan-Newman algorithm 11.4 Modularity optimization 11.5 Label propagation 11.6 Directed, disconnected and valued data 11.7 Large data 11.8 Computational considerations 11.9 Summary 11.10 Problems and exercises 12 Equivalence 12.1 12.2 12.3 12.4 12.5 Introduction Structural equivalence Profile similarity Blockmodels Optimization 214 215 219 222 226 227 228 228 229 229 231 282 232 235 241 244
CONTENTS 12.6 Regular equivalence 12.7 The REGE algorithm 12.8 Core-periphery models 12.9 Summary 12.10 Problems and exercises 13 Analyzing Two-mode Data 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 Introduction Converting to one-mode data Converting valued two-mode matrices to one-mode Bipartite networks Subgroups and community detection Core-periphery models Equivalence Summary Problems and exercises 14 Introduction to Inferential Statistics for Complete Networks 14.1 14.2 14.3 14.4 14.5 14.6 14.7 Introduction Levels of analysis Statistical tests at the group level Statistical tests at the node level Statistical tests at the dyad level Summary Problems and exercises IS ERGMs and SAOMs 15.1 15.2 15.3 15.4 15.5 15.6 15.7 General introduction to ERGMs and the interpretation of parameters Obtaining (approximate) maximum likelihood estimates for an ERGM Parameter selection and goodness of fit Directed networks Stochastic actor-oriented models Summary Problems and exercises Glossary Overview of datasets used Overview ofR functions used References Index 246 248 250 256 256 259 260 261 266 266 269 272 273 277 277 279 280 280 281 283 284 291 291 293 294 303 309 313 314 322 323 325 335 337 341 351
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| adam_txt |
Contents About the authors Preface Glossary of symbols Online resources 1 Introduction 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 Why networks? What are networks? Types of relations Goals of analysis Network variables as explanatory variables Network variables as outcome variables Summary Problems and exercises 2 Mathematical Foundations 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 Introduction Graphs Paths and components Adjacency matrices Ways and modes Matrix products Summary Problems and exercises 3 Research Design 3.1 Introduction 3.2 Experiments and field studies 3.3 Whole-network and personal-network research designs 3.4 Sources of network data 3.5 Types of nodes and types of ties 3.6 Actor attributes 3.7 Sampling and bounding 3.8 Sources of data reliability and validity issues 3.9 Ethical considerations 3.10 Summary 3.11 Problems and exercises xi xiii xvii xix 1 2 2 4 8 9 11 12 12 15 16 16 19 22 24 25 27 27 29 30 30 33 34 35 38 38 41 45 47 47
viil 1 CONTENTS 4 Data Collection , . 4.1 Introduction 4.2 Network questions 4.3 Question formats 4.4 Interviewee burden 4.5 Data collection and reliability 4.6 Archival data collection 4.7 Data from electronic sources 4.8 Summary 4.9 Problems and exercises 49 50 58 62 69 5 Data Management 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 Introduction The R program Data storage Importing and storing data in R Data transformation for network data Converting attributes to matrices Storing, transforming and exporting network data and results Summary Problems and exercises б Multivariate Techniques Used in Network Analysis 6.1 6.2 6.3 6.4 6.5 6.6 Introduction Multidimensional scaling Correspondence analysis Hierarchical clustering Summary Problems and exercises 7 Visualization 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 Introduction Layout Embedding node attributes Embedding tie attributes Node filtering and ego networks Closing comments Summary Problems and exercises 22 73 78 88 91 Ю0 102 ЮЗ 103 1θ7 108 108 110 114 117 117 119 120 120 132 136 140 14շ 1^2 143 8 Local Node-level Measures 8.1 Introduction ott. . 8.2 Tie composition 8.3 Valued tie composition , . 146
CONTENTS 8.4 8.5 8.6 8.7 8.8 Alter composition Ego-alter similarity Ego-network structural shape measures Summary Problems and exercises 9 Centrality 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 Introduction Basic concept Undirected, non-valued networks Directed, non-valued networks Valued networks Negative tie networks Induced centralities Summary Problems and exercises 10 Group-level Measures ļ ix 152 156 161 166 167 169 170 170 171 183 187 188 189 190 190 193 Introduction Measures based on local properties Measures based on global properties Centralization and core-peripheriness Attribute-based measures Summary Problems and exercises 194 195 201 205 207 210 211 11 Subgroups and Community Detection 213 10.1 10.2 10.3 10.4 10.5 10.6 10.7 11.1 Introduction 11.2 Cliques 11.3 Girvan-Newman algorithm 11.4 Modularity optimization 11.5 Label propagation 11.6 Directed, disconnected and valued data 11.7 Large data 11.8 Computational considerations 11.9 Summary 11.10 Problems and exercises 12 Equivalence 12.1 12.2 12.3 12.4 12.5 Introduction Structural equivalence Profile similarity Blockmodels Optimization 214 215 219 222 226 227 228 228 229 229 231 282 232 235 241 244
CONTENTS 12.6 Regular equivalence 12.7 The REGE algorithm 12.8 Core-periphery models 12.9 Summary 12.10 Problems and exercises 13 Analyzing Two-mode Data 13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 Introduction Converting to one-mode data Converting valued two-mode matrices to one-mode Bipartite networks Subgroups and community detection Core-periphery models Equivalence Summary Problems and exercises 14 Introduction to Inferential Statistics for Complete Networks 14.1 14.2 14.3 14.4 14.5 14.6 14.7 Introduction Levels of analysis Statistical tests at the group level Statistical tests at the node level Statistical tests at the dyad level Summary Problems and exercises IS ERGMs and SAOMs 15.1 15.2 15.3 15.4 15.5 15.6 15.7 General introduction to ERGMs and the interpretation of parameters Obtaining (approximate) maximum likelihood estimates for an ERGM Parameter selection and goodness of fit Directed networks Stochastic actor-oriented models Summary Problems and exercises Glossary Overview of datasets used Overview ofR functions used References Index 246 248 250 256 256 259 260 261 266 266 269 272 273 277 277 279 280 280 281 283 284 291 291 293 294 303 309 313 314 322 323 325 335 337 341 351 |
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| index_date | 2024-07-03T19:26:10Z |
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| spelling | Borgatti, Stephen P. 1956- Verfasser (DE-588)1035396157 aut Analyzing social networks using R Stephen P. Borgatti, Martin G. Everett, Jeffrey C. Johnson, Filip Agneessens Los Angeles ; London ; New Delhi ; Singapore ; Washington DC ; Melbourne SAGE [2022] © 2022 xviii, 359 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Netzwerkanalyse Soziologie (DE-588)4205975-6 s R Programm (DE-588)4705956-4 s DE-604 Everett, Martin G. Verfasser (DE-588)1035396300 aut Johnson, Jeffrey C. Verfasser (DE-588)1035795310 aut Agneessens, Filip Verfasser (DE-588)1035684845 aut Erscheint auch als Online-Ausgabe 978-1-5297-6658-5 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033273159&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Borgatti, Stephen P. 1956- Everett, Martin G. Johnson, Jeffrey C. Agneessens, Filip Analyzing social networks using R Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4205975-6 (DE-588)4705956-4 |
| title | Analyzing social networks using R |
| title_auth | Analyzing social networks using R |
| title_exact_search | Analyzing social networks using R |
| title_exact_search_txtP | Analyzing social networks using R |
| title_full | Analyzing social networks using R Stephen P. Borgatti, Martin G. Everett, Jeffrey C. Johnson, Filip Agneessens |
| title_fullStr | Analyzing social networks using R Stephen P. Borgatti, Martin G. Everett, Jeffrey C. Johnson, Filip Agneessens |
| title_full_unstemmed | Analyzing social networks using R Stephen P. Borgatti, Martin G. Everett, Jeffrey C. Johnson, Filip Agneessens |
| title_short | Analyzing social networks using R |
| title_sort | analyzing social networks using r |
| topic | Netzwerkanalyse Soziologie (DE-588)4205975-6 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Netzwerkanalyse Soziologie R Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033273159&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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