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E-raamat: Analyzing Social Networks Using R

  • Formaat: 384 pages
  • Ilmumisaeg: 21-Apr-2022
  • Kirjastus: Sage Publications Ltd
  • Keel: eng
  • ISBN-13: 9781529766585
  • Formaat - PDF+DRM
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  • Formaat: 384 pages
  • Ilmumisaeg: 21-Apr-2022
  • Kirjastus: Sage Publications Ltd
  • Keel: eng
  • ISBN-13: 9781529766585

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This approachable book introduces network research in R, walking you through every step of doing social network analysis.

This approachable book introduces network research in R, walking you through every step of doing social network analysis. Drawing together research design, data collection and data analysis, it explains the core concepts of network analysis in a non-technical way.

The book balances an easy to follow explanation of the theoretical and statistical foundations underpinning network analysis with practical guidance on key steps like data management, preparation and visualisation. With clarity and expert insight, it:

• Discusses measures and techniques for analyzing social network data, including digital media 
• Explains a range of statistical models including QAP and ERGM, giving you the tools to approach different types of networks
• Offers digital resources like practice datasets and worked examples that help you get to grips with R software

About the authors xi
Preface xiii
Glossary of symbols xvii
Online resources xix
1 Introduction 1(14)
1.1 Why networks?
2(1)
1.2 What are networks?
2(2)
1.3 Types of relations
4(4)
1.4 Goals of analysis
8(1)
1.5 Network variables as explanatory variables
9(2)
1.6 Network variables as outcome variables
11(1)
1.7 Summary
12(1)
1.8 Problems and exercises
12(3)
2 Mathematical Foundations 15(14)
2.1 Introduction
16(1)
2.2 Graphs
16(3)
2.3 Paths and components
19(3)
2.4 Adjacency matrices
22(2)
2.5 Ways and modes
24(1)
2.6 Matrix products
25(2)
2.7 Summary
27(1)
2.8 Problems and exercises
27(2)
3 Research Design 29(20)
3.1 Introduction
30(1)
3.2 Experiments and field studies
30(3)
3.3 Whole-network and personal-network research designs
33(1)
3.4 Sources of network data
34(1)
3.5 Types of nodes and types of ties
35(3)
3.6 Actor attributes
38(1)
3.7 Sampling and bounding
38(3)
3.8 Sources of data reliability and validity issues
41(4)
3.9 Ethical considerations
45(2)
3.10 Summary
47(1)
3.11 Problems and exercises
47(2)
4 Data Collection 49(22)
4.1 Introduction
50(1)
4.2 Network questions
50(2)
4.3 Question formats
52(5)
4.4 Interviewee burden
57(1)
4.5 Data collection and reliability
58(2)
4.6 Archival data collection
60(2)
4.7 Data from electronic sources
62(7)
4.8 Summary
69(1)
4.9 Problems and exercises
69(2)
5 Data Management 71(36)
5.1 Introduction
72(1)
5.2 The R program
73(5)
5.3 Data storage
78(10)
5.4 Importing and storing data in R
88(3)
5.5 Data transformation for network data
91(9)
5.6 Converting attributes to matrices
100(2)
5.7 Storing, transforming and exporting network data and results
102(1)
5.8 Summary
103(1)
5.9 Problems and exercises
103(4)
6 Multivariate Techniques Used in Network Analysis 107(12)
6.1 Introduction
108(1)
6.2 Multidimensional scaling
108(2)
6.3 Correspondence analysis
110(4)
6.4 Hierarchical clustering
114(3)
6.5 Summary
117(1)
6.6 Problems and exercises
117(2)
7 Visualization 119(26)
7.1 Introduction
120(1)
7.2 Layout
120(12)
7.3 Embedding node attributes
132(4)
7.4 Embedding tie attributes
136(4)
7.5 Node filtering and ego networks
140(2)
7.6 Closing comments
142(1)
7.7 Summary
142(1)
7.8 Problems and exercises
143(2)
8 Local Node-level Measures 145(24)
8.1 Introduction
146(2)
8.2 Tie composition
148(2)
8.3 Valued tie composition
150(2)
8.4 Alter composition
152(4)
8.5 Ego-alter similarity
156(5)
8.6 Ego-network structural shape measures
161(5)
8.7 Summary
166(1)
8.8 Problems and exercises
167(2)
9 Centrality 169(24)
9.1 Introduction
170(1)
9.2 Basic concept
170(1)
9.3 Undirected, non-valued networks
171(12)
9.4 Directed, non-valued networks
183(4)
9.5 Valued networks
187(1)
9.6 Negative tie networks
188(1)
9.7 Induced centralities
189(1)
9.8 Summary
190(1)
9.9 Problems and exercises
190(3)
10 Group-level Measures 193(20)
10.1 Introduction
194(1)
10.2 Measures based on local properties
195(6)
10.3 Measures based on global properties
201(4)
10.4 Centralization and core-peripheriness
205(2)
10.5 Attribute-based measures
207(3)
10.6 Summary
210(1)
10.7 Problems and exercises
211(2)
11 Subgroups and Community Detection 213(18)
11.1 Introduction
214(1)
11.2 Cliques
215(4)
11.3 Girvan-Newman algorithm
219(3)
11.4 Modularity optimization
222(4)
11.5 Label propagation
226(1)
11.6 Directed, disconnected and valued data
227(1)
11.7 Large data
228(1)
11.8 Computational considerations
228(1)
11.9 Summary
229(1)
11.10 Problems and exercises
229(2)
12 Equivalence 231(28)
12.1 Introduction
232(1)
12.2 Structural equivalence
232(3)
12.3 Profile similarity
235(6)
12.4 Blockmodels
241(3)
12.5 Optimization
244(2)
12.6 Regular equivalence
246(2)
12.7 The REGE algorithm
248(2)
12.8 Core-periphery models
250(6)
12.9 Summary
256(1)
12.10 Problems and exercises
256(3)
13 Analyzing Two-mode Data 259(20)
13.1 Introduction
260(1)
13.2 Converting to one-mode data
261(5)
13.3 Converting valued two-mode matrices to one-mode
266(1)
13.4 Bipartite networks
266(3)
13.5 Subgroups and community detection
269(3)
13.6 Core-periphery models
272(1)
13.7 Equivalence
273(4)
13.8 Summary
277(1)
13.9 Problems and exercises
277(2)
14 Introduction to Inferential Statistics for Complete Networks 279(14)
14.1 Introduction
280(1)
14.2 Levels of analysis
280(1)
14.3 Statistical tests at the group level
281(2)
14.4 Statistical tests at the node level
283(1)
14.5 Statistical tests at the dyad level
284(7)
14.6 Summary
291(1)
14.7 Problems and exercises
291(2)
15 ERGMs and SAOMs 293(32)
15.1 General introduction to ERGMs and the interpretation of parameters
294(9)
15.2 Obtaining (approximate) maximum likelihood estimates for an ERGM
303(6)
15.3 Parameter selection and goodness of fit
309(4)
15.4 Directed networks
313(1)
15.5 Stochastic actor-oriented models
314(8)
15.6 Summary
322(1)
15.7 Problems and exercises
323(2)
Glossary 325(10)
Overview of datasets used 335(2)
Overview of R functions used 337(4)
References 341(10)
Index 351
Stephen Borgatti is the Gatton Endowed Chair of Management at the Gatton College of Business and Economics at the University of Kentucky. He has published extensively in management journals, as well cross-disciplinary journals such as Science and Social Networks. He has published over 100 peer-reviewed articles on network analysis, garnering more than 70,000 Google Scholar citations. With Martin Everett, Steve is co-author of UCINET, a well-known software package for social network analysis, as well as founder of the annual LINKS Center workshop on social network analysis. He is also a 2-term past President of INSNA (the professional association for network researchers) and winner of their Simmel Award for lifetime achievement. Martin Everett is Professor of Social Network Analysis and co-director of the Mitchell Centre for SNA at the University of Manchester. He has published extensively on social network analysis and has over 100 peer-reviewed articles and consulted with government agencies as well as public and private companies. With Stephen Borgatti, Martin is co-author of UCINET, a well-known software package for social network analysis and is co-editor of the journal Social Networks. He is also a past President of INSNA (the professional association for network researchers) and winner of their Simmel Award for lifetime achievement. He was elected as an academician to the UK Academy of Social Sciences in 2004.  Jeffrey Johnson is a University Term Professor of Anthropology at the University of Florida. He was a former Program Manager with the Army Research Office (IPA) where he started the basic science research program in the social sciences.  He has conducted extensive long-term research, supported by the National Science Foundation, comparing group dynamics and the evolution of social networks of over-wintering crews at the American South Pole Station, with those at the Polish, Russian, Chinese, and Indian Antarctic Stations. In related research, he has studied aspects of team cognition and social networks on success in simulated space missions. He has published extensively in anthropological, sociological, biological, aerospace, and marine science journals and was the founding editor of the Journal of Quantitative Anthropology, co-editor of the journal Human Organization, and  the author of Selecting Ethnographic Informants, Sage, 1990. Filip Agneessens is an Associate Professor at the Department of Sociology and Social Research, University of Trento. He has published on a diversity of topics related to social networks, including measures of centrality, statistical models, ego-networks and social support, two-mode networks, negative ties, multilevel networks and issues related to data collection. He has also applied social network analysis to understand the antecedents and consequences of interactions among employees, and in particular within teams. Together with Martin Everett, he was a guest-editor for a special issue on Advances in Two-mode Social Network Analysis in the journal Social Networks, and together with Nick Harrigan and Joe Labianca he guest-edited a special issue on Negative and Signed Tie Networks. He has taught numerous introductory and advanced social network courses and workshops over the last 15 years.