Table of Contents
Table of Contents
Every chapter and section below links straight into the free online edition.
Preface
xi
Acknowledgments
xv
Main Text
1
| 1.1 What is Social Media Mining | 1 |
| 1.2 New Challenges for Mining | 2 |
| 1.3 Book Overview and Reader’s Guide | 3 |
| 1.4 Summary | 6 |
| 1.5 Bibliographic Notes | 7 |
| 1.6 Exercises | 8 |
Part I
Essentials
| 3.1 Centrality | 52 |
| 3.2 Transitivity and Reciprocity | 64 |
| 3.3 Balance and Status | 69 |
| 3.4 Similarity | 71 |
| 3.5 Summary | 76 |
| 3.6 Bibliographic Notes | 77 |
| 3.7 Exercises | 78 |
| 4.1 Properties of Real-World Networks | 80 |
| 4.2 Random Graphs | 84 |
| 4.3 Small-World Model | 93 |
| 4.4 Preferential Attachment Model | 97 |
| 4.5 Summary | 101 |
| 4.6 Bibliographic Notes | 102 |
| 4.7 Exercises | 103 |
| 5.1 Data | 106 |
| 5.2 Data Preprocessing | 111 |
| 5.3 Data Mining Algorithms | 113 |
| 5.4 Supervised Learning | 113 |
| 5.5 Unsupervised Learning | 127 |
| 5.6 Summary | 133 |
| 5.7 Bibliographic Notes | 134 |
| 5.8 Exercises | 135 |
Part II
Communities and Interactions
| 6.1 Community Detection | 144 |
| 6.2 Community Evolution | 161 |
| 6.3 Community Evaluation | 168 |
| 6.4 Summary | 174 |
| 6.5 Bibliographic Notes | 175 |
| 6.6 Exercises | 176 |
| 7.1 Herd Behavior | 181 |
| 7.2 Information Cascades | 186 |
| 7.3 Diffusion of Innovations | 193 |
| 7.4 Epidemics | 200 |
| 7.5 Summary | 209 |
| 7.6 Bibliographic Notes | 210 |
| 7.7 Exercises | 212 |
Part III
Applications
| 8.1 Measuring Assortativity | 218 |
| 8.2 Influence | 225 |
| 8.3 Homophily | 234 |
| 8.4 Distinguishing Influence and Homophily | 236 |
| 8.5 Summary | 240 |
| 8.6 Bibliographic Notes | 241 |
| 8.7 Exercises | 242 |
| 10.1 Individual Behavior | 271 |
| 10.2 Collective Behavior | 283 |
| 10.3 Summary | 290 |
| 10.4 Bibliographic Notes | 291 |
| 10.5 Exercises | 292 |
Notes
295
Bibliography
299
Index
315