About the authors |
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xi | |
Preface |
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xiii | |
Glossary of symbols |
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xvii | |
Online resources |
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xix | |
1 Introduction |
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1 | (14) |
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2 | (1) |
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2 | (2) |
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4 | (4) |
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8 | (1) |
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1.5 Network variables as explanatory variables |
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9 | (2) |
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1.6 Network variables as outcome variables |
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11 | (1) |
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12 | (1) |
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1.8 Problems and exercises |
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12 | (3) |
2 Mathematical Foundations |
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15 | (14) |
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16 | (1) |
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16 | (3) |
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19 | (3) |
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22 | (2) |
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24 | (1) |
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25 | (2) |
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27 | (1) |
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2.8 Problems and exercises |
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27 | (2) |
3 Research Design |
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29 | (20) |
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30 | (1) |
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3.2 Experiments and field studies |
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30 | (3) |
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3.3 Whole-network and personal-network research designs |
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33 | (1) |
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3.4 Sources of network data |
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34 | (1) |
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3.5 Types of nodes and types of ties |
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35 | (3) |
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38 | (1) |
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3.7 Sampling and bounding |
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38 | (3) |
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3.8 Sources of data reliability and validity issues |
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41 | (4) |
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3.9 Ethical considerations |
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45 | (2) |
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47 | (1) |
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3.11 Problems and exercises |
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47 | (2) |
4 Data Collection |
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49 | (22) |
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50 | (1) |
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50 | (2) |
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52 | (5) |
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57 | (1) |
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4.5 Data collection and reliability |
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58 | (2) |
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4.6 Archival data collection |
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60 | (2) |
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4.7 Data from electronic sources |
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62 | (7) |
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69 | (1) |
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4.9 Problems and exercises |
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69 | (2) |
5 Data Management |
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71 | (36) |
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72 | (1) |
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73 | (5) |
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78 | (10) |
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5.4 Importing and storing data in R |
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88 | (3) |
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5.5 Data transformation for network data |
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91 | (9) |
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5.6 Converting attributes to matrices |
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100 | (2) |
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5.7 Storing, transforming and exporting network data and results |
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102 | (1) |
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103 | (1) |
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5.9 Problems and exercises |
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103 | (4) |
6 Multivariate Techniques Used in Network Analysis |
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107 | (12) |
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108 | (1) |
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6.2 Multidimensional scaling |
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108 | (2) |
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6.3 Correspondence analysis |
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110 | (4) |
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6.4 Hierarchical clustering |
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114 | (3) |
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117 | (1) |
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6.6 Problems and exercises |
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117 | (2) |
7 Visualization |
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119 | (26) |
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120 | (1) |
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120 | (12) |
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7.3 Embedding node attributes |
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132 | (4) |
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7.4 Embedding tie attributes |
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136 | (4) |
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7.5 Node filtering and ego networks |
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140 | (2) |
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142 | (1) |
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142 | (1) |
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7.8 Problems and exercises |
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143 | (2) |
8 Local Node-level Measures |
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145 | (24) |
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146 | (2) |
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148 | (2) |
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8.3 Valued tie composition |
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150 | (2) |
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152 | (4) |
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156 | (5) |
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8.6 Ego-network structural shape measures |
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161 | (5) |
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166 | (1) |
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8.8 Problems and exercises |
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167 | (2) |
9 Centrality |
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169 | (24) |
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170 | (1) |
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170 | (1) |
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9.3 Undirected, non-valued networks |
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171 | (12) |
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9.4 Directed, non-valued networks |
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183 | (4) |
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187 | (1) |
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9.6 Negative tie networks |
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188 | (1) |
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189 | (1) |
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190 | (1) |
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9.9 Problems and exercises |
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190 | (3) |
10 Group-level Measures |
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193 | (20) |
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194 | (1) |
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10.2 Measures based on local properties |
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195 | (6) |
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10.3 Measures based on global properties |
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201 | (4) |
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10.4 Centralization and core-peripheriness |
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205 | (2) |
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10.5 Attribute-based measures |
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207 | (3) |
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210 | (1) |
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10.7 Problems and exercises |
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211 | (2) |
11 Subgroups and Community Detection |
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213 | (18) |
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214 | (1) |
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215 | (4) |
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11.3 Girvan-Newman algorithm |
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219 | (3) |
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11.4 Modularity optimization |
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222 | (4) |
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226 | (1) |
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11.6 Directed, disconnected and valued data |
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227 | (1) |
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228 | (1) |
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11.8 Computational considerations |
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228 | (1) |
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229 | (1) |
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11.10 Problems and exercises |
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229 | (2) |
12 Equivalence |
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231 | (28) |
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232 | (1) |
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12.2 Structural equivalence |
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232 | (3) |
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235 | (6) |
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241 | (3) |
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244 | (2) |
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246 | (2) |
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248 | (2) |
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12.8 Core-periphery models |
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250 | (6) |
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256 | (1) |
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12.10 Problems and exercises |
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256 | (3) |
13 Analyzing Two-mode Data |
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259 | (20) |
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260 | (1) |
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13.2 Converting to one-mode data |
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261 | (5) |
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13.3 Converting valued two-mode matrices to one-mode |
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266 | (1) |
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266 | (3) |
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13.5 Subgroups and community detection |
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269 | (3) |
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13.6 Core-periphery models |
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272 | (1) |
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273 | (4) |
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277 | (1) |
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13.9 Problems and exercises |
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277 | (2) |
14 Introduction to Inferential Statistics for Complete Networks |
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279 | (14) |
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280 | (1) |
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280 | (1) |
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14.3 Statistical tests at the group level |
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281 | (2) |
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14.4 Statistical tests at the node level |
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283 | (1) |
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14.5 Statistical tests at the dyad level |
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284 | (7) |
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291 | (1) |
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14.7 Problems and exercises |
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291 | (2) |
15 ERGMs and SAOMs |
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293 | (32) |
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15.1 General introduction to ERGMs and the interpretation of parameters |
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294 | (9) |
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15.2 Obtaining (approximate) maximum likelihood estimates for an ERGM |
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303 | (6) |
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15.3 Parameter selection and goodness of fit |
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309 | (4) |
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313 | (1) |
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15.5 Stochastic actor-oriented models |
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314 | (8) |
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322 | (1) |
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15.7 Problems and exercises |
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323 | (2) |
Glossary |
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325 | (10) |
Overview of datasets used |
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335 | (2) |
Overview of R functions used |
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337 | (4) |
References |
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341 | (10) |
Index |
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351 | |