Handbook of Social Network Technologies and Applications -

Handbook of Social Network Technologies and Applications (eBook)

Borko Furht (Herausgeber)

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2010 | 2010
XVIII, 716 Seiten
Springer US (Verlag)
978-1-4419-7142-5 (ISBN)
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Social networking is a concept that has existed for a long time; however, with the explosion of the Internet, social networking has become a tool for people to connect and communicate in ways that were impossible in the past. The recent development of Web 2.0 has provided many new applications, such as Myspace, Facebook, and LinkedIn.

The purpose of Handbook of Social Network Technologies and Applications is to provide comprehensive guidelines on the current and future trends in social network technologies and applications in the field of Web-based Social Networks. This handbook includes contributions from world experts in the field of social networks from both academia and private industry. A number of crucial topics are covered including Web and software technologies and communication technologies for social networks. Web-mining techniques, visualization techniques, intelligent social networks, Semantic Web, and many other topics are covered. Standards for social networks, case studies, and a variety of applications are covered as well.



Borko Furht is a professor and chairman of the Department of Computer & Electrical Engineering and Computer Science at Florida Atlantic University (FAU) in Boca Raton, Florida. Professor Furht received his Ph.D. in electrical and computer engineering from the University of Belgrade. His current research is in multimedia systems, video coding and compression, 3D video and image systems, video databases, wireless multimedia, and Internet computing. He is a founder and editor-in-chief of the Journal of Multimedia Tools and Applications (Springer). He has received several technical and publishing awards, and has consulted for many high-tech companies including IBM, Hewlett-Packard, Xerox, General Electric, JPL, NASA, Honeywell, and RCA, and has been an expert witness for Cisco and Qualcomm. He has given many invited talks, keynote lectures, seminars, and tutorials, and served on the Board of Directors of several high-tech companies.


Social networking is a concept that has existed for a long time; however, with the explosion of the Internet, social networking has become a tool for people to connect and communicate in ways that were impossible in the past. The recent development of Web 2.0 has provided many new applications, such as Myspace, Facebook, and LinkedIn. The purpose of Handbook of Social Network Technologies and Applications is to provide comprehensive guidelines on the current and future trends in social network technologies and applications in the field of Web-based Social Networks. This handbook includes contributions from world experts in the field of social networks from both academia and private industry. A number of crucial topics are covered including Web and software technologies and communication technologies for social networks. Web-mining techniques, visualization techniques, intelligent social networks, Semantic Web, and many other topics are covered. Standards for social networks, case studies, and a variety of applications are covered as well.

Borko Furht is a professor and chairman of the Department of Computer & Electrical Engineering and Computer Science at Florida Atlantic University (FAU) in Boca Raton, Florida. Professor Furht received his Ph.D. in electrical and computer engineering from the University of Belgrade. His current research is in multimedia systems, video coding and compression, 3D video and image systems, video databases, wireless multimedia, and Internet computing. He is a founder and editor-in-chief of the Journal of Multimedia Tools and Applications (Springer). He has received several technical and publishing awards, and has consulted for many high-tech companies including IBM, Hewlett-Packard, Xerox, General Electric, JPL, NASA, Honeywell, and RCA, and has been an expert witness for Cisco and Qualcomm. He has given many invited talks, keynote lectures, seminars, and tutorials, and served on the Board of Directors of several high-tech companies.

Preface 8
Editor-in-Chief 10
Contents 12
Contributors 16
Part I Social Media Analysis and Organization 20
Chapter 1 


21 
1.1 Introduction 21
1.2 Social Network Analysis: Definition and Features 22
1.3 The Development of Social Network Analysis: A Brief History 26
1.4 Basic Concepts of Social Network Analysis 30
1.4.1 Ties 30
1.4.2 Density 31
1.4.3 Path, Length, and Distance 31
1.4.4 Centrality 32
1.4.5 Clique 33
1.5 Research of SNA: Design, Theorization, and Data Processing 33
1.5.1 Designing a Social Network Analysis 33
1.5.2 Theorization in Social Network Analysis 35
1.5.3 SNA Data Processing Tools 36
1.6 Summary 37
References 38
Chapter 2 



40 
2.1 Introduction 40
2.2 Analysis Strategy 41
2.3 Analysis of Social Networks Based on Traffic Data of Internet Access Service Offered Over Cellular Phones 42
2.3.1 Data To Be Analyzed 43
2.3.2 Definition of Symbols and Problem Description 44
2.3.3 How People Subscribed to the Service I and the Structure of Social Networks 45
2.4 Analysis of Social Networks Based on the Number of SNS Users 49
2.4.1 Analyzed Data 49
2.4.2 Growth in the Number of SNS Users and Social Networks 50
2.5 Verification of Degree Distribution of Social Networks 55
2.6 Conclusions 56
References 58
A Relationship Between the Number of Links and the Volume of Traffic 58
B Behavior of 1-cx(m) 60
Chapter 3 


61 
3.1 Motivation, or Who Follows Whom 61
3.2 The Social Web as a Tensor 64
3.2.1 The TweetRank Model 64
3.2.2 PARAFAC for Authority Ranking 65
3.2.3 Ranking Example 67
3.3 Implementation 67
3.3.1 Data Collection and Transformation 67
3.3.2 Analysis 69
3.3.3 Use Case Example 70
3.4 Related Work 71
3.4.1 Rating Web Pages 71
3.4.2 Rating (Semi-)Structured Data 72
3.5 Conclusion 73
References 73
Chapter 4 


75 
4.1 Introduction 75
4.2 Characteristics of Online Communication 78
4.2.1 Background 78
4.2.2 Communication Modes in Social Networks 79
4.2.3 Prior Work on Communication Modalities 80
4.3 Rich Media Communication Patterns 81
4.3.1 Problem Formulation 84
4.3.1.1 Definitions 84
4.3.1.2 Data Model 84
4.3.1.3 Problem Statement 85
4.3.2 Conversational Themes 86
4.3.2.1 Chunk-Based Mixture Model of Themes 86
4.3.3 Interestingness 88
4.3.3.1 Interestingness of Participants 89
4.3.3.2 Interestingness of Conversations 90
4.3.3.3 Joint Optimization of Interestingness 91
4.3.4 Consequences of Interestingness 92
4.3.5 Experimental Studies 93
4.4 Information Diffusion 96
4.4.1 Preliminaries 97
4.4.1.1 Social Graph Model 97
4.4.1.2 Attribute Homophily 98
4.4.1.3 Topic Diffusion 98
4.4.1.4 Diffusion Series 99
4.4.2 Problem Statement 100
4.4.3 Characterizing Diffusion 101
4.4.4 Prediction Framework 102
4.4.5 Predicting Hidden States 104
4.4.6 Predicting Observed Action 104
4.4.7 Distortion Measurement 105
4.4.8 Experimental Studies 106
4.5 Summary and Future Work 107
References 108
Chapter 5 


111 
5.1 Introduction 111
5.2 What Is a Commercial Social Network Profile? 112
5.2.1 Reciprocal Identification 113
5.2.2 Types of Commercial Social Network Profiles 114
5.2.2.1 Music 114
5.2.2.2 Films 114
5.2.2.3 Television 115
5.2.2.4 Public Figures 115
5.2.2.5 Events 115
5.3 Quantitative Analysis of CSNPs 116
5.3.1 Connections 116
5.3.2 Interactions 117
5.3.3 Hit Counters 118
5.3.4 Updates 118
5.4 Qualitative Analysis of CSNPs 118
5.4.1 Connections 119
5.4.1.1 Valid Connections 119
5.4.1.2 Connection Initiation 120
5.4.1.3 Connection Demographics 120
5.4.1.4 Friend Stacking 121
5.4.1.5 Div Overlaying 122
5.4.2 Interactions 122
5.4.2.1 Interaction Type, Source, and Content 122
5.4.2.2 Interaction Based Spam 124
5.4.2.3 Interaction Frequency and Timing 125
5.4.2.4 Interaction Uniqueness 127
5.5 Technical Notes 127
5.6 Summary 127
References 128
Chapter 6 


130 
6.1 Introduction 130
6.2 Social Networks 131
6.2.1 Social Network Analysis 132
6.2.2 Discovering Structure of Networks 132
6.2.2.1 Finding Communities in Social Networks 133
6.2.2.2 Finding Patterns in Social Networks 133
6.3 SNA from Log Files 134
6.3.1 Log File Analysis 134
6.4 Data Mining Methods Related to SNA and Log Mining 136
6.4.1 Clustering Techniques 137
6.4.1.1 Partitional Clustering 138
6.4.1.2 Hierarchical Clustering 140
6.4.2 Discovering of Network Evolution 143
6.4.3 Finding Overlapping Communities 144
6.5 Application of SNA 144
6.5.1 Web Mining 144
6.5.2 Phone Social Networks 145
6.5.3 Mail Logs, Server Logs 146
6.5.4 Business Sphere 146
6.5.5 Education 147
6.6 Case Study: Finding Students' Patterns of Behavior in LMS Moodle 149
6.6.1 Dataset Description 149
6.6.2 Experiment and Results 150
6.7 Conclusions 154
References 156
Chapter 7 


162 
7.1 Introduction 162
7.2 Definitions and Background 163
7.2.1 Completeness 164
7.2.2 Certainty 164
7.2.3 Bias 166
7.3 Dolphin Societies 167
7.3.1 Shark Bay Data Collection 167
7.3.2 Fission Fussion Societies 168
7.3.3 Advantages and Disadvantages of Non-Human Studies 168
7.4 Completeness of Network-Sampling Subjects and Collecting Enough Data 169
7.4.1 Sampling Options 169
7.4.2 Sampling Methods Comparison 170
7.4.3 Amount of Data per Subject Necessary 171
7.4.4 Recommendations 172
7.5 Identifying Uncertainties and Biases 172
7.5.1 Uncertain Subjects and Behaviors 172
7.5.2 Observers Reliability and Consistency 173
7.5.3 Time and Behavioral Sampling 175
7.5.4 Depth and Association Sampling 175
7.5.5 Hidden Behaviors or Social Encounters 176
7.5.6 Observers Can Affect the Behaviors They Monitor 177
7.5.7 Recommendations 177
7.6 Computational Approaches to Improve Data Quality for Social Network Analysis 179
7.7 Final Thoughts 181
References 181
Chapter 8 


184 
8.1 Introduction 184
8.1.1 Temporal Variation of a Social Network 186
8.2 Web as a Social Network 186
8.2.1 Concept of Web Graph 187
8.2.1.1 Definition 187
8.2.1.2 Properties 188
8.3 Evolution of Web Graph 190
8.4 Dynamic Web Graph Model 192
8.4.1 Dynamic Data Model Preliminaries 193
8.4.2 Temporal Structure-Based Schema 194
8.5 Conclusion and Future Works 197
References 198
9 Churn in Social Networks 200
9.1 Introduction 200
9.2 Understanding Churn in Social Networks 202
9.2.1 Reasons for Churn 203
9.2.2 Churn in Digital Social Networks 204
9.3 Definitions of Churn in Digital Social Networks 205
9.4 Empirical Analysis 209
9.4.1 Data Set 1: User Activity in a Discussion Board 209
9.4.2 Data Set 2: Activity in an Online Social Network 213
9.4.3 Summary 215
9.5 Models for Churn Prediction 216
9.5.1 Feature-Based Approaches 216
9.5.2 Social Network Analysis for Churn Prediction 218
9.6 Network Effects and Propagation of Churn 219
9.6.1 Network Views 219
9.6.2 Diffusion Models 220
9.6.3 Combining Feature-Based Approaches and Diffusion Models 223
9.7 Popularity and Influence in Social Networks 225
9.7.1 Social Roles and Influence in Discussion Boards 225
9.7.2 Popularity in Online Social Networks 227
9.8 Summary and Conclusion 231
References 232
Part II Social Media Mining and Search 236
Chapter 10 


237 
10.1 Introduction 237
10.2 Contextual Dependency from Social Contexts 240
10.2.1 Network Separation: Divide 242
10.2.2 Network Superposition: Conquer 242
10.3 Social Network Ontology 243
10.3.1 Similarity-Based Ontology Alignment 243
10.3.2 Consensual Ontology Discovery 244
10.4 Interactive Discovery of Social Networks 246
10.5 Context-Based Service 250
10.6 Related Work and Discussion 251
10.7 Concluding Remarks and Future Work 251
References 252
Chapter 11 

254 
11.1 Introduction 254
11.2 Background on Digital Identities 256
11.2.1 Civil vs. Digital Identities 256
11.2.2 The People Identification Problem 257
11.2.3 Requirements on Digital Identities 259
11.2.4 Classes of Digital Identities 262
11.2.5 Taxonomy of Approaches to Identities 263
11.3 Putting Social Relations to Work 264
11.3.1 Overview 264
11.3.2 User Authentication Using Social Relations 264
11.3.3 Connection Establishment Using Social Relations 266
11.3.4 Malware Propagation and Social Relations 266
11.4 Social Digital Identity 268
11.4.1 Overview 268
11.4.2 Generating a Seed Digital Identity 268
11.4.3 Binding a Person to a SDI Token 270
11.4.4 Example Deployment Scenario for SDI 272
11.4.5 Example Applications of SDI 273
11.5 Information and Threats in Social Networks 275
11.5.1 Information on Social Networks 275
11.5.2 Information for Establishing Identity 276
11.5.3 Identity vs. Privacy 277
11.6 Summary 278
References 278
Chapter 12 

281 
12.1 Introduction 281
12.2 Definition of Community 282
12.2.1 Local definitions 282
12.2.2 Global definitions 282
12.2.3 Definitions Based on Vertex Similarity 283
12.3 Evaluating Communities 283
12.4 Methods for Community Detection 285
12.4.1 Divisive Algorithms 285
12.4.2 Modularity Optimization 285
12.4.3 Spectral Algorithms 286
12.4.4 Other Algorithms 286
12.5 Tools for Detecting Communities 286
12.5.1 Tools for Large-Scale Networks 287
12.5.2 Tools for Interactive Analysis 287
12.6 Conclusion 291
References 292
Chapter 13 


293 
13.1 Introduction 293
13.2 Related Works 295
13.3 Public Attention Based Video Concept Discovery and Categorization for Video Searching 296
13.4 Dataset Collection 298
13.5 Data Pre-Processing 298
13.5.1 Data Cleaning 298
13.5.2 Text Matrix Generation 299
13.6 Video Processing via Clustering 299
13.6.1 Video Clustering and Concept Discovery 300
13.6.2 Factorized Component Entropy Measures for Vocabulary Construction 303
13.7 Experimental Evaluation 306
13.7.1 Empirical Setting 306
13.7.2 Video Categories and Concepts 307
13.7.3 User Comments vs. User Tags 310
13.8 Conclusion and Future Work 312
References 313
Chapter 14 


315 
14.1 Introduction 315
14.2 Related Work 316
14.3 Proposed Approach 318
14.3.1 Overview 318
14.3.2 Filtering Irrelevant Images 319
14.3.2.1 Image Representation 319
14.3.2.2 Visual Clustering 319
14.3.2.3 Selecting the Most Relevant Clusters 320
14.3.3 Detecting Representative Regions 320
14.3.4 Generating Representative Photographs 321
14.4 Experimental Results 321
14.4.1 Quantitative Evaluation 322
14.4.2 Examples of Regional Representative Photos 323
14.5 Conclusion and Future Work 327
References 327
Chapter 15 


329 
15.1 Introduction 329
15.2 Recommender Systems 330
15.3 Memory-Based Methods of Collaborative Filtering 331
15.4 Choosing Variable Number of Neighbors for Each User 333
15.4.1 Example 337
15.5 The Coverage Improvement 338
15.6 Evaluation of Our Techniques 340
15.7 Conclusions 341
References 341
Chapter 16 


343 
16.1 Introduction 343
16.2 Methodologies of Network Community Mining 344
16.2.1 Optimization Based Algorithms 344
16.2.2 Heuristic Methods 347
16.2.3 Other Methods 349
16.3 Applications of Community Mining Algorithms 349
16.3.1 Network Reduction 350
16.3.2 Discovering Scientific Collaboration Groups from Social Networks 352
16.3.3 Mining Communities from Distributed and Dynamic Networks 355
16.4 Conclusions 356
References 356
Part III Social Network Infrastructures and Communities 359
Chapter 17 

360 
17.1 Introduction 360
17.1.1 Scope of the Chapter 362
17.2 Challenges for DOSN 362
17.2.1 Differences to Other Decentralized or P2P Applications 366
17.3 The Case for Decentralizing OSNs 367
17.4 General Purpose DOSNs 370
17.4.1 Proposed DOSN Approaches 371
17.5 Specialized Application Centric DOSNs 376
17.5.1 Social-Based P2P File Sharing 377
17.5.2 Shared Bookmarks and Collaborative Search 379
17.5.3 Micro-Blogging 379
17.6 Social Distributed Systems 380
17.6.1 Social DHT: SocialCircle 381
17.6.2 Storage/Back-up 383
17.7 Delay-Tolerant DOSN 384
17.8 Conclusion 386
References 387
Chapter 18 


390 
18.1 Introduction 390
18.2 Actions, Networking and Community Formation 394
18.2.1 Mutual Awareness and Community Discovery 395
18.2.2 Extracting Communities Based on Mutual Awareness Structure 395
18.2.2.1 Computable Definition for Mutual Awareness 396
18.2.2.2 Mutual Awareness Expansion 397
18.2.3 Application: Query-Sensitive Community Extraction 400
18.3 Analyzing Communities and Evolutions in Dynamic Network 402
18.3.1 Sustained Membership, Evolution and Community Discovery 402
18.3.2 Extracting Sustained Evolving Communities 403
18.3.2.1 Problem Formulation 403
18.3.2.2 Extracting Communities and Evolutions 405
18.3.3 Application: Time-Dependent Ranking in Communities 406
18.4 Community Analysis on Multi-Relational Social Data 407
18.4.1 Embeddedness, Artifacts and Community Discovery 409
18.4.2 Extracting Communities from Rich-Context Social Networks 409
18.4.2.1 Problem Formulation 409
18.4.2.2 Metagraph Factorization 412
18.4.2.3 Time Evolving Extension 413
18.4.3 Application: Context-Sensitive Prediction in Enterprise 414
18.5 Conclusions and Future Directions 416
References 418
Chapter 19 

420 
19.1 Introduction 420
19.2 Social Websites and Their User Interfaces 422
19.2.1 Facebook Lite 422
19.3 WEB Accessibility Analysis 426
19.3.1 The Main Principles and Structure of WCAG 2.0 426
19.3.1.1 Structure of WCAG 2.0 426
19.3.1.2 Guideline 1. Perceivable 427
19.3.1.3 Guideline 2. Operable 427
19.3.1.4 Guideline 3. Understandable 427
19.3.1.5 Guideline 4. Robust 428
19.3.2 The XValid Software 428
19.4 Compare the Results with Other Website's Accessibility 434
19.5 Conclusions 435
References 436
Chapter 20 


437 
20.1 Introduction 437
20.2 User Data Management, Inference and Distribution 438
20.3 Enabling New Human Experiences 439
20.3.1 The Technologies 440
20.3.1.1 Social Networks 440
20.3.1.2 Reality Mining 440
20.3.1.3 Context-Awareness 440
20.3.2 Architectural Framework and Methodology 441
20.3.2.1 Data Management 441
20.3.2.2 Knowledge Generation 442
20.3.2.3 Service Exposure and Control 444
20.3.3 Innovations 444
20.4 The Social Enabler 445
20.4.1 The Algorithms 447
20.4.1.1 Distance 447
20.4.1.2 Similarity 447
20.4.1.3 Influence 448
20.4.1.4 Adjustments 448
20.4.2 Technological Considerations 450
20.5 Applications 450
20.5.1 The Augmented Social Experience 451
20.5.2 Future Self-Awareness 452
20.5.3 Advertising 453
20.6 Conclusions and Future Work 454
References 454
Chapter 21 


456 
21.1 Introduction 456
21.2 An Introduction to Relational Network Theory 457
21.3 Computing with Words 460
21.4 On the Concept of Node Importance 461
21.5 Generalizing the Concept of a Cluster 463
21.6 Congested Nodes 466
21.7 Duration 471
21.8 Directed Graphs 471
21.9 Authority Figures 472
21.10 Conclusion 475
References 475
Part IV Privacy in Online Social Networks 477
Chapter 22 

478 
22.1 Introduction 478
22.2 Online Social Networks 480
22.3 Trust in Online Environment 482
22.4 Related Work 483
22.5 Trust Models Based on Subjective Logic 486
22.6 Trust Network Analysis 487
22.6.1 Operators for Deriving Trust 488
22.6.2 Trust Path Dependency and Network Simplification 489
22.7 Trust Transitivity Analysis 490
22.7.1 Uncertainty Favoring Trust Transitivity 490
22.7.2 Opposite Belief Favoring 491
22.7.3 Base Rate Sensitive Transitivity 492
22.7.4 Mass Hysteria 493
22.8 The Dirichlet Reputation System 494
22.9 Combining Trust and Reputation 497
22.10 Trust Derivation Based on Trust Comparisons 499
22.11 Conclusion 501
References 501
Chapter 23 

504 
23.1 Introduction 504
23.1.1 Social Network Providers and Their Customers 506
23.1.2 Functional Overview of Online Social Networks 508
23.1.3 Modelling Data Contained in Online Social Networks 510
23.1.4 A Model for Social Network Services 513
23.2 Security Objectives: Privacy, Integrity, and Availability 513
23.2.1 Privacy 515
23.2.2 Integrity 516
23.2.3 Availability 516
23.3 Attack Spectrum and Countermeasures 517
23.3.1 Plain Impersonation 518
23.3.2 Profile Cloning 519
23.3.3 Profile Hijacking 519
23.3.4 Profile Porting 520
23.3.5 ID Theft 520
23.3.6 Profiling 521
23.3.7 Secondary Data Collection 522
23.3.8 Fake Requests 522
23.3.9 Crawling and Harvesting 523
23.3.10 Image Retrieval and Analysis 523
23.3.11 Communication Tracking 524
23.3.12 Fake Profiles and Sybil Attacks 524
23.3.13 Group Metamorphosis 525
23.3.14 Ballot Stuffing and Defamation 525
23.3.15 Censorship 526
23.3.16 Collusion Attacks 526
23.4 Summary and Conclusion 527
References 528
Chapter 24 


530 
24.1 Introduction 530
24.2 SNA Survey 533
24.3 SNA Measures 536
24.3.1 Degree 536
24.3.2 Betweenness 537
24.3.3 Closeness 538
24.4 Bayes Probability Theorem 538
24.5 Analysis & Results
24.6 Conclusion 550
References 552
Chapter 25 


555 
25.1 Introduction 555
25.2 Background 556
25.2.1 Epidemic Propagation in Social Networks 556
25.2.2 Centrality Indexes 557
25.2.2.1 Degree Centrality 557
25.2.2.2 Group Degree Centrality 558
25.2.2.3 Closeness Centrality 559
25.2.2.4 Group Closeness Centrality 559
25.2.2.5 Betweenness Centrality 559
25.2.2.6 Group Betweenness Centrality 560
25.2.2.7 Random Walk Betweenness Centrality 560
25.3 Experimental Setup 561
25.3.1 Extracting the Social Network 561
25.3.2 Pinpointing Central Users 562
25.3.3 Simulation 563
25.4 Experiment Results 564
25.4.1 Threat Prevalence 564
25.4.2 Epidemic Half-Life 566
25.4.3 Detection Time 567
25.4.4 Intercepted Threats 567
25.4.5 IDS Effectiveness 569
25.5 Summary and Conclusions 572
References 573
Chapter 26 


575 
26.1 Introduction 575
26.2 Context, Threats, and Incidents 577
26.3 Two Patterns 578
26.3.1 Participation-Collaboration 578
26.3.1.1 Intent 578
26.3.1.2 Example 579
26.3.1.3 Context 579
26.3.1.4 Problem 579
26.3.1.5 Solution 579
26.3.1.6 Dynamics 580
26.3.1.7 Implementation 581
26.3.1.8 Known Uses 581
26.3.1.9 Related Patterns 582
26.3.1.10 Consequences 582
26.3.2 Collaborative Tagging 582
26.3.2.1 Intent 582
26.3.2.2 Example 582
26.3.2.3 Context 582
26.3.2.4 Problem 583
26.3.2.5 Solution 583
26.3.2.6 Known Uses 585
26.3.2.7 Consequences 585
26.4 Improvements 585
26.5 Conclusions 586
References 587
Part V Visualisation and Applications of Social Networks 589
Chapter 27 

590 
27.1 Introduction 590
27.2 Social Network Analysis 592
27.2.1 Graph Theory 592
27.2.2 Centrality 593
27.2.3 Clustering 594
27.3 Visualization 595
27.3.1 Node-Edge Diagrams 595
27.3.1.1 Random Layout 595
27.3.1.2 Force-Directed Layout 596
27.3.1.3 Tree Layout 596
27.3.2 Matrix Representations 598
27.4 Visualizing Online Social Networks 599
27.4.1 Web Communities 599
27.4.2 Email Groups 602
27.4.3 Digital Libraries 604
27.4.3.1 Co-Authorship Networks 604
27.4.3.2 Co-Citation Relations 606
27.4.4 Web 2.0 Services 609
27.4.5 Summary 612
27.5 Conclusions 613
References 614
Chapter 28 


616 
28.1 Introduction 616
28.2 Node-Link Diagrams 617
28.3 Scaling to Larger Networks 619
28.3.1 Reducing the Quantity of Information 619
28.3.2 Incremental Exploration 621
28.3.3 Using More Visual Space 622
28.3.4 Alternative Representations 623
28.4 Adjacency Matrix Representations 624
28.4.1 Reordering 625
28.4.2 Navigation 626
28.5 Visualizing Social Networks with Matrix-Based Representations 627
28.5.1 Matrix or Node-Link Diagram? 627
28.5.2 Matrix + Node-Link Diagrams 628
28.5.2.1 Initiate Exploration 629
28.5.2.2 Explore Interactively 630
28.5.2.3 Find a Consensus in the Data 631
28.5.2.4 Present Findings 632
28.5.3 Hybrid Representations 632
28.5.3.1 Augmenting Matrices 632
28.5.3.2 Merging Matrix and Node-Link Diagram 635
28.6 Conclusion 637
References 639
Chapter 29 

642 
29.1 Introduction 642
29.2 Social Network Analysis 643
29.3 Applications of Social Network Analysis 644
29.3.1 Organizational Issues 645
29.3.2 Recommendation and E-commerce Systems 647
29.3.3 Covert Networks 648
29.3.4 Web Applications 649
29.3.5 Community Welfare 650
29.3.6 Collaboration Networks 651
29.3.7 Co-Citation Networks 652
29.4 Conclusion 653
References 653
Chapter 30 

655 
30.1 Introduction 655
30.1.1 Online Advertising 656
30.2 Identifying the Social Network Effect in Online Advertising 658
30.2.1 Homophily 658
30.2.1.1 Similarity Between Friends 659
30.2.1.2 Are Similar Users Friends? 659
30.2.2 Influencers 660
30.2.2.1 Modeling the Spread of Influence 660
30.2.2.2 Leveraging Rich Data for Social Network Based Marketing 662
30.3 Online Ad Targeting 662
30.3.1 Targeting Based on User Information 664
30.3.1.1 Contextual Targeting 664
30.3.1.2 User Segment Targeting 665
30.3.1.3 Behavioral Targeting 665
30.3.2 Social Network Targeting 666
30.3.2.1 Using Peer-Pressure for Targeting 666
30.3.2.2 Using Friends for Targeting 666
30.3.2.3 Using Social Features for Targeting 667
30.3.2.4 Targeting in Social Neighborhoods 668
30.3.3 Combining User Features and Social Network Features 668
30.3.3.1 Weighted Combination of Scores 669
30.3.3.2 Ensemble Classifier 669
30.4 Applications of Social Network Advertising 670
30.4.1 Yahoo! Instant Messenger Social Network 671
30.4.1.1 Yahoo! IM Graph Statistics 671
30.4.1.2 Conversations in the IM Social Network 672
30.4.2 Predicting Ad Clicks 673
30.4.2.1 Dataset Description 674
30.4.2.2 Measuring the Social Network Effect in Ad Clicks 674
30.4.2.3 Modeling Propensity to Click on Ads 676
30.4.3 Predicting Product Adoption in Social Networks 678
30.4.3.1 Dataset Description 679
30.4.3.2 Measuring the Social Network Effect in Product Adoption 681
30.4.3.3 Modeling the Propensity to Adopt the PC to Phone Product 687
30.5 Conclusion 690
References 691
Chapter 31 


694 
31.1 Introduction 694
31.2 Review of Literature 696
31.3 The Study 699
31.3.1 Overview of the Research Center 699
31.3.2 Overview of the Social Bookmarking Tool (SBT) 700
31.3.3 Data Collection and Analysis 702
31.4 Discussion 708
References 712
Index 715

Erscheint lt. Verlag 4.11.2010
Zusatzinfo XVIII, 716 p.
Verlagsort New York
Sprache englisch
Themenwelt Mathematik / Informatik Informatik Datenbanken
Mathematik / Informatik Informatik Netzwerke
Informatik Software Entwicklung User Interfaces (HCI)
Mathematik / Informatik Informatik Theorie / Studium
Informatik Weitere Themen Hardware
Wirtschaft Betriebswirtschaft / Management Wirtschaftsinformatik
Schlagworte currentjm • Fuzzy social networks • Intelligent social network modeling • Internet • Intranet • Modularity of social network communities • Online • P2P infrastructure for social networks • Preserving privacy in social networks • social network analysis • Social network profile an • Social Networks • Standards • Visualization • visualizing social n • Web mining techniques for social networks
ISBN-10 1-4419-7142-4 / 1441971424
ISBN-13 978-1-4419-7142-5 / 9781441971425
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