Relationships and the pattern of relationships have a large and varied influence on both individual and group action. The fundamental distinction of social network analysis research is that relationships are of paramount importance in explaining behavior. Because of this, social network analysis offers many exciting tools and techniques for research and practice in a wide variety of medical and public health situations including organizational improvements, understanding risk behaviors, coordinating coalitions, and the delivery of health care services. This book provides an introduction to the major theories, methods, models, and findings of social network analysis research and application. In three sections, it presents a comprehensive overview of the topic; first in a survey of its historical and theoretical foundations, then in practical descriptions of the variety of methods currently in use, and finally in a discussion of its specific applications for behavior change in a public health context. Throughout, the text has been kept clear, concise, and comprehensible, with short mathematical formulas for some key indicators or concepts. Researchers and students alike will find it an invaluable resource for understanding and implementing social network analysis in their own practice.
Author(s): Thomas W. Valente
Edition: 1
Year: 2010
Language: English
Pages: 296
Contents......Page 12
Part I: Models......Page 16
Relationships Matter......Page 18
Random Sampling Is Not Enough......Page 21
Literature Overview......Page 22
Major Research Advances......Page 24
Individual- and Network-Level Measures......Page 36
Summary......Page 40
2 History......Page 41
History Reconsidered......Page 43
Behavioral Science......Page 45
Life Span Approaches......Page 50
Public Health and Medical Applications......Page 51
Summary......Page 54
3 Methods......Page 56
Data Collection Techniques......Page 58
Data Management......Page 65
Network Variables......Page 70
Summary......Page 75
4 Ego- and Personal-Network Effects......Page 76
Measures......Page 80
Statistical Analysis......Page 85
Personal Network versus Sociometric Variables......Page 87
Snowball/Sequenced Data......Page 89
Summary......Page 92
Part II: Measures......Page 94
5 Centrality......Page 96
Degree......Page 97
Closeness......Page 98
Distances for Unconnected Nodes......Page 100
Betweenness......Page 102
Correlation among Centrality Measures......Page 106
Link or Edge Centrality......Page 108
Centrality versus Centralization......Page 109
Centrality and Behavior......Page 110
Characteristics of Opinion Leaders......Page 113
Summary......Page 114
6 Groups......Page 115
Components and K-Cores......Page 116
Girvan-Newman Technique......Page 120
Groups and Behavior......Page 123
Group Membership and Disease......Page 124
Groups, Density, and Bridges......Page 125
Summary......Page 128
7 Positions......Page 129
Network-Level Positions......Page 130
CONCOR......Page 136
Individual Positional Measures......Page 137
Individual Measures as Positions......Page 138
Positions and Behavior......Page 139
Network Weights......Page 140
Summary......Page 141
8 Network-Level Measures......Page 143
Density......Page 144
Mutuality/Reciprocity......Page 145
Triads/Transitivity......Page 147
Diameter/Average Path Length......Page 149
Density and Cohesion......Page 150
Clustering......Page 152
Centralization......Page 153
Core-Periphery......Page 155
Two-Mode Data......Page 159
Individual Network-Level Interactions......Page 161
Summary......Page 162
Part III: Applications......Page 164
9 Exponential Random Graph Models, P* and Actor Oriented Models......Page 166
Vectorizing the Matrix......Page 168
Exponential Random Graph Models (ERGM)......Page 171
Simulation......Page 172
New Specifications......Page 175
Obesity Example......Page 176
Actor-Oriented Model......Page 178
WINCART......Page 181
Summary......Page 185
10 Diffusion of Innovations......Page 187
Homogeneous Mixing......Page 190
Integration and Opinion Leadership......Page 194
Structural Models......Page 196
Dynamic Models......Page 199
Empirical Estimates Using Diffusion Network Data......Page 202
Infection and Susceptibility......Page 205
Thresholds......Page 206
Summary......Page 209
11 Network Interventions......Page 211
Opinion Leaders......Page 212
Key Players......Page 216
Groups......Page 217
Identifying Leaders and Groups......Page 218
Snowball Sampling or Network Recruitment......Page 220
Rewiring Networks......Page 222
Bridges and Potential Bridges......Page 223
Links versus Nodes......Page 224
Networks and Attributes......Page 227
Iatrogenic Effects......Page 229
A Pharmaceutical Marketing Example......Page 231
Summary......Page 233
12 Summary......Page 234
Agent-Based Modeling......Page 239
Increasing the Threshold......Page 246
Network Scale......Page 247
Future Research Questions......Page 248
Limitations......Page 252
Conclusion......Page 253
Appendix A: Glossary......Page 254
Appendix B: Sample Sociometric Survey......Page 256
Appendix C: Sample Egocentric Survey......Page 258
Appendix D: Centrality Scores for Network in Figure 1–1......Page 260
Appendix E: Input Files (Network and Attribute) for the Network in Figure 1–1......Page 262
References......Page 266
B......Page 284
H......Page 285
P......Page 286
Z......Page 287
C......Page 288
H......Page 289
P......Page 290
U......Page 291
W......Page 292
