Preface |
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x | |
Acknowledgments |
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xiii | |
About the Author |
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xiv | |
About the Book |
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xv | |
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1 Concepts in Network Science |
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1 | (22) |
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1 | (1) |
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2 | (1) |
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3 | (2) |
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1.3.1 A History in Social Studies |
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4 | (1) |
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5 | (7) |
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1.4.1 Characteristics of Networks |
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7 | (1) |
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1.4.2 Properties of Networks |
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7 | (1) |
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8 | (3) |
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11 | (1) |
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12 | (9) |
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1.5.1 Data Structure for Network Analysis and Network Optimization |
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13 | (1) |
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1.5.1.1 Multilink and Self-Link |
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14 | (1) |
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1.5.1.2 Loading and Unloading the Graph |
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15 | (1) |
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1.5.2 Options for Network Analysis and Network Optimization Procedures |
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15 | (1) |
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16 | (1) |
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1.5.3.1 Analyzing the Summary Statistics for the Les Miserables Network |
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17 | (4) |
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21 | (2) |
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23 | (78) |
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23 | (3) |
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25 | (1) |
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26 | (9) |
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2.2.1 Finding the Connected Components |
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27 | (8) |
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2.3 Biconnected Components |
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35 | (3) |
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2.3.1 Finding the Biconnected Components |
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36 | (2) |
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38 | (20) |
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2.4.1 Finding Communities |
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45 | (13) |
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58 | (4) |
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59 | (3) |
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62 | (8) |
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2.6.1 Finding the Reach Network |
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65 | (5) |
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70 | (7) |
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2.7.1 Finding the Network Projection |
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72 | (5) |
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77 | (11) |
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2.8.1 Computing Node Similarity |
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82 | (6) |
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88 | (10) |
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2.9.1 Searching for Subgraphs Matches |
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91 | (7) |
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98 | (3) |
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101 | (66) |
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101 | (1) |
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3.2 Network Metrics of Power and Influence |
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102 | (1) |
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103 | (11) |
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3.3.1 Computing Degree Centrality |
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103 | (7) |
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3.3.2 Visualizing a Network |
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110 | (4) |
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114 | (7) |
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3.4.1 Computing the Influence Centrality |
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115 | (6) |
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3.5 Clustering Coefficient |
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121 | (3) |
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3.5.1 Computing the Clustering Coefficient Centrality |
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121 | (3) |
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124 | (5) |
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3.6.1 Computing the Closeness Centrality |
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124 | (5) |
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3.7 Betweenness Centrality |
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129 | (7) |
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3.7.1 Computing the Between Centrality |
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130 | (6) |
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3.8 Eigenvector Centrality |
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136 | (8) |
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3.8.1 Computing the Eigenvector Centrality |
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137 | (7) |
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144 | (7) |
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3.9.1 Computing the PageRank Centrality |
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144 | (7) |
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151 | (6) |
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3.10.1 Computing the Hub and Authority Centralities |
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152 | (5) |
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3.11 Network Centralities Calculation by Group |
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157 | (7) |
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3.11.1 By Group Network Centralities |
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158 | (6) |
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164 | (3) |
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167 | (104) |
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167 | (3) |
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167 | (3) |
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4.1.2 Network Optimization in SAS Viya |
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170 | (1) |
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170 | (6) |
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172 | (4) |
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176 | (3) |
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177 | (2) |
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179 | (6) |
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4.4.1 Finding the Minimum Weight Matching in a Worker-Task Problem |
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181 | (4) |
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4.5 Minimum-Cost Network Flow |
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185 | (9) |
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4.5.1 Finding the Minimum-Cost Network Flow in a Demand-Supply Problem |
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188 | (6) |
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4.6 Maximum Network Flow Problem |
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194 | (5) |
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4.6.1 Finding the Maximum Network Flow in a Distribution Problem |
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195 | (4) |
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199 | (6) |
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4.7.1 Finding the Minimum Cuts |
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201 | (4) |
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4.8 Minimum Spanning Tree |
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205 | (3) |
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4.8.1 Finding the Minimum Spanning Tree |
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206 | (2) |
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208 | (12) |
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211 | (9) |
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220 | (15) |
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4.10.1 Finding Shortest Paths |
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223 | (12) |
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235 | (4) |
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4.11.1 Finding the Transitive Closure |
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236 | (3) |
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4.12 Traveling Salesman Problem |
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239 | (10) |
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4.12.1 Finding the Optimal Tour |
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243 | (6) |
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4.13 Vehicle Routing Problem |
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249 | (16) |
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4.13.1 Finding the Optimal Vehicle Routes for a Delivery Problem |
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253 | (12) |
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265 | (3) |
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4.14.1 Finding the Topological Sort in a Directed Graph |
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266 | (2) |
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268 | (3) |
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5 Real-World Applications in Network Science |
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271 | (58) |
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271 | (1) |
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5.2 An Optimal Tour Considering a Multimodal Transportation System - The Traveling Salesman Problem Example in Paris |
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272 | (13) |
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5.3 An Optimal Beer Kegs Distribution - The Vehicle Routing Problem Example in Asheville |
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285 | (13) |
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5.4 Network Analysis and Supervised Machine Learning Models to Predict COVID-19 Outbreaks |
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298 | (8) |
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5.5 Urban Mobility in Metropolitan Cities |
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306 | (6) |
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5.6 Fraud Detection in Auto Insurance Based on Network Analysis |
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312 | (8) |
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5.7 Customer Influence to Reduce Churn and Increase Product Adoption |
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320 | (4) |
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5.8 Community Detection to Identify Fraud Events in Telecommunications |
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324 | (4) |
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328 | (1) |
Index |
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329 | |