Social Media Mining

Table of contents

- Cover (Page 1)
- Half title (Page 3)
- Title (Page 5)
- Copyright (Page 6)
- Dedication (Page 7)
- Contents (Page 9)
- Preface (Page 13)
- Acknowledgments (Page 17)
- 1 Introduction (Page 19)
- 1.1 What is Social Media Mining (Page 19)
- 1.2 New Challenges for Mining (Page 20)
- 1.3 Book Overview and Reader's Guide (Page 21)
- 1.4 Summary (Page 24)
- 1.5 Bibliographic Notes (Page 25)
- 1.6 Exercises (Page 26)
- Part I Essentials (Page 29)
- 2 Graph Essentials (Page 31)
- 2.1 Graph Basics (Page 32)
- 2.1.1 Nodes (Page 32)
- 2.1.2 Edges (Page 32)
- 2.1.3 Degree and Degree Distribution (Page 33)
- 2.2 Graph Representation (Page 36)
- 2.3 Types of Graphs (Page 38)
- 2.4 Connectivity in Graphs (Page 40)
- 2.5 Special Graphs (Page 44)
- 2.5.1 Trees and Forests (Page 45)
- 2.5.2 Special Subgraphs (Page 45)
- 2.5.3 Complete Graphs (Page 46)
- 2.5.4 Planar Graphs (Page 47)
- 2.5.5 Bipartite Graphs (Page 47)
- 2.5.6 Regular Graphs (Page 49)
- 2.5.7 Bridges (Page 49)
- 2.6 Graph Algorithms (Page 49)
- 2.6.1 Graph/Tree Traversal (Page 50)
- 2.6.2 Shortest Path Algorithms (Page 53)
- 2.6.3 Minimum Spanning Trees (Page 55)
- 2.6.4 Network Flow Algorithms (Page 56)
- 2.6.5 Maximum Bipartite Matching (Page 62)
- 2.6.6 Bridge Detection (Page 63)
- 2.7 Summary (Page 64)
- 2.8 Bibliographic Notes (Page 65)
- 2.9 Exercises (Page 66)
- 3 Network Measures (Page 69)
- 3.1 Centrality (Page 70)
- 3.1.1 Degree Centrality (Page 70)
- 3.1.2 Eigenvector Centrality (Page 71)
- 3.1.3 Katz Centrality (Page 74)
- 3.1.4 PageRank (Page 76)
- 3.1.5 Betweenness Centrality (Page 77)
- 3.1.6 Closeness Centrality (Page 79)
- 3.1.7 Group Centrality (Page 81)
- 3.2 Transitivity and Reciprocity (Page 82)
- 3.2.1 Transitivity (Page 83)
- 3.2.2 Reciprocity (Page 86)
- 3.3 Balance and Status (Page 87)
- 3.4 Similarity (Page 89)
- 3.4.1 Structural Equivalence (Page 90)
- 3.4.2 Regular Equivalence (Page 92)
- 3.5 Summary (Page 94)
- 3.6 Bibliographic Notes (Page 95)
- 3.7 Exercises (Page 96)
- 4 Network Models (Page 98)
- 4.1 Properties of Real-World Networks (Page 98)
- 4.1.1 Degree Distribution (Page 99)
- 4.1.2 Clustering Coefficient (Page 101)
- 4.1.3 Average Path Length (Page 102)
- 4.2 Random Graphs (Page 102)
- 4.2.1 Evolution of Random Graphs (Page 104)
- 4.2.2 Properties of Random Graphs (Page 107)
- 4.2.3 Modeling Real-World Networks with Random Graphs (Page 110)
- 4.3 Small-World Model (Page 111)
- 4.3.1 Properties of the Small-World Model (Page 113)
- 4.3.2 Modeling Real-World Networks with the Small-World Model (Page 115)
- 4.4 Preferential Attachment Model (Page 115)
- 4.4.1 Properties of the Preferential Attachment Model (Page 117)
- 4.4.2 Modeling Real-World Networks with the Preferential Attachment Model (Page 119)
- 4.5 Summary (Page 119)
- 4.6 Bibliographic Notes (Page 120)
- 4.7 Exercises (Page 121)
- 5 Data Mining Essentials (Page 123)
- 5.1 Data (Page 124)
- 5.1.1 Data Quality (Page 128)
- 5.2 Data Preprocessing (Page 129)
- 5.3 Data Mining Algorithms (Page 131)
- 5.4 Supervised Learning (Page 131)
- 5.4.1 Decision Tree Learning (Page 133)
- 5.4.2 Naive Bayes Classifier (Page 135)
- 5.4.3 Nearest Neighbor Classifier (Page 137)
- 5.4.4 Classification with Network Information (Page 137)
- 5.4.5 Regression (Page 140)
- 5.4.6 Supervised Learning Evaluation (Page 144)
- 5.5 Unsupervised Learning (Page 145)
- 5.5.1 Clustering Algorithms (Page 146)
- 5.5.2 Unsupervised Learning Evaluation (Page 148)
- 5.6 Summary (Page 151)
- 5.7 Bibliographic Notes (Page 152)
- 5.8 Exercises (Page 153)
- Part II Communities and Interactions (Page 157)
- 6 Community Analysis (Page 159)
- 6.1 Community Detection (Page 162)
- 6.1.1 Community Detection Algorithms (Page 163)
- 6.1.2 Member-Based Community Detection (Page 163)
- 6.1.3 Group-Based Community Detection (Page 171)
- 6.2 Community Evolution (Page 179)
- 6.2.1 How Networks Evolve (Page 180)
- 6.2.2 Community Detection in Evolving Networks (Page 183)
- 6.3 Community Evaluation (Page 186)
- 6.3.1 Evaluation with Ground Truth (Page 186)
- 6.3.2 Evaluation without Ground Truth (Page 190)
- 6.4 Summary (Page 192)
- 6.5 Bibliographic Notes (Page 193)
- 6.6 Exercises (Page 194)
- 7 Information Diffusion in Social Media (Page 197)
- 7.1 Herd Behavior (Page 199)
- 7.1.1 Bayesian Modeling of Herd Behavior (Page 201)
- 7.1.2 Intervention (Page 204)
- 7.2 Information Cascades (Page 204)
- 7.2.1 Independent Cascade Model (ICM) (Page 205)
- 7.2.2 Maximizing the Spread of Cascades (Page 207)
- 7.2.3 Intervention (Page 210)
- 7.3 Diffusion of Innovations (Page 211)
- 7.3.1 Innovation Characteristics (Page 211)
- 7.3.2 Diffusion of Innovations Models (Page 211)
- 7.3.3 Modeling Diffusion of Innovations (Page 214)
- 7.3.4 Intervention (Page 218)
- 7.4 Epidemics (Page 218)
- 7.4.1 Definitions (Page 220)
- 7.4.2 SI Model (Page 220)
- 7.4.3 SIR Model (Page 222)
- 7.4.4 SIS Model (Page 224)
- 7.4.5 SIRS Model (Page 225)
- 7.4.6 Intervention (Page 226)
- 7.5 Summary (Page 227)
- 7.6 Bibliographic Notes (Page 228)
- 7.7 Exercises (Page 230)
- Part III Applications (Page 233)
- 8 Influence and Homophily (Page 235)
- 8.1 Measuring Assortativity (Page 236)
- 8.1.1 Measuring Assortativity for Nominal Attributes (Page 237)
- 8.1.2 Measuring Assortativity for Ordinal Attributes (Page 240)
- 8.2 Influence (Page 243)
- 8.2.1 Measuring Influence (Page 243)
- 8.2.2 Modeling Influence (Page 247)
- 8.3 Homophily (Page 252)
- 8.3.1 Measuring Homophily (Page 252)
- 8.3.2 Modeling Homophily (Page 253)
- 8.4 Distinguishing Influence and Homophily (Page 254)
- 8.4.1 Shuffle Test (Page 254)
- 8.4.2 Edge-Reversal Test (Page 255)
- 8.4.3 Randomization Test (Page 256)
- 8.5 Summary (Page 258)
- 8.6 Bibliographic Notes (Page 259)
- 8.7 Exercises (Page 260)
- 9 Recommendation in Social Media (Page 263)
- 9.1 Challenges (Page 264)
- 9.2 Classical Recommendation Algorithms (Page 265)
- 9.2.1 Content-Based Methods (Page 265)
- 9.2.2 Collaborative Filtering (CF) (Page 266)
- 9.2.3 Extending Individual Recommendation toGroups of Individuals (Page 273)
- 9.3 Recommendation Using Social Context (Page 276)
- 9.3.1 Using Social Context Alone (Page 276)
- 9.3.2 Extending Classical Methods with Social Context (Page 277)
- 9.3.3 Recommendation Constrained by Social Context (Page 279)
- 9.4 Evaluating Recommendations (Page 281)
- 9.4.1 Evaluating Accuracy of Predictions (Page 281)
- 9.4.2 Evaluating Relevancy of Recommendations (Page 282)
- 9.4.3 Evaluating Ranking of Recommendations (Page 284)
- 9.5 Summary (Page 285)
- 9.6 Bibliographic Notes (Page 286)
- 9.7 Exercises (Page 287)
- 10 Behavior Analytics (Page 289)
- 10.1 Individual Behavior (Page 289)
- 10.1.1 Individual Behavior Analysis (Page 291)
- 10.1.2 Individual Behavior Modeling (Page 295)
- 10.1.3 Individual Behavior Prediction (Page 296)
- 10.2 Collective Behavior (Page 301)
- 10.2.1 Collective Behavior Analysis (Page 301)
- 10.2.2 Collective Behavior Modeling (Page 306)
- 10.2.3 Collective Behavior Prediction (Page 306)
- 10.3 Summary (Page 308)
- 10.4 Bibliographic Notes (Page 309)
- 10.5 Exercises (Page 310)
- Notes (Page 313)
- Chapter 1 (Page 313)
- Chapter 2 (Page 313)
- Chapter 3 (Page 313)
- Chapter 4 (Page 313)
- Chapter 5 (Page 314)
- Chapter 6 (Page 314)
- Chapter 7 (Page 314)
- Chapter 8 (Page 314)
- Chapter 9 (Page 315)
- Bibliography (Page 317)
- Index (Page 333)