Furht Handbook of Social Network Technologies and Applications

1. Auflage 2010
ISBN: 978-1-4419-7142-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 716 Seiten

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.

Autoren/Hrsg.

Furht, Borko

Inhaltsverzeichnis

1;Preface;8
2;Editor-in-Chief;10
3;Contents;12
4;Contributors;16
5;Part I Social Media Analysis and Organization;20
5.1;Chapter 1
Social Network Analysis: History, Concepts,
and Research
;21
5.1.1;1.1 Introduction;21
5.1.2;1.2 Social Network Analysis: Definition and Features;22
5.1.3;1.3 The Development of Social Network Analysis: A Brief History;26
5.1.4;1.4 Basic Concepts of Social Network Analysis;30
5.1.4.1;1.4.1 Ties;30
5.1.4.2;1.4.2 Density;31
5.1.4.3;1.4.3 Path, Length, and Distance;31
5.1.4.4;1.4.4 Centrality;32
5.1.4.5;1.4.5 Clique;33
5.1.5;1.5 Research of SNA: Design, Theorization, and Data Processing;33
5.1.5.1;1.5.1 Designing a Social Network Analysis;33
5.1.5.2;1.5.2 Theorization in Social Network Analysis;35
5.1.5.3;1.5.3 SNA Data Processing Tools;36
5.1.6;1.6 Summary;37
5.1.7;References;38
5.2;Chapter 2
Structure and Dynamics of Social Networks
Revealed by Data Analysis of Actual
Communication Services
;40
5.2.1;2.1 Introduction;40
5.2.2;2.2 Analysis Strategy;41
5.2.3;2.3 Analysis of Social Networks Based on Traffic Data of Internet Access Service Offered Over Cellular Phones;42
5.2.3.1;2.3.1 Data To Be Analyzed;43
5.2.3.2;2.3.2 Definition of Symbols and Problem Description;44
5.2.3.3;2.3.3 How People Subscribed to the Service I and the Structure of Social Networks;45
5.2.4;2.4 Analysis of Social Networks Based on the Number of SNS Users;49
5.2.4.1;2.4.1 Analyzed Data;49
5.2.4.2;2.4.2 Growth in the Number of SNS Users and Social Networks;50
5.2.5;2.5 Verification of Degree Distribution of Social Networks;55
5.2.6;2.6 Conclusions;56
5.2.7;References;58
5.2.8;A Relationship Between the Number of Links and the Volume of Traffic;58
5.2.9;B Behavior of 1-cx(m);60
5.3;Chapter 3
Analysis of Social Networks by Tensor
Decomposition
;61
5.3.1;3.1 Motivation, or Who Follows Whom;61
5.3.2;3.2 The Social Web as a Tensor;64
5.3.2.1;3.2.1 The TweetRank Model;64
5.3.2.2;3.2.2 PARAFAC for Authority Ranking;65
5.3.2.3;3.2.3 Ranking Example;67
5.3.3;3.3 Implementation;67
5.3.3.1;3.3.1 Data Collection and Transformation;67
5.3.3.2;3.3.2 Analysis;69
5.3.3.3;3.3.3 Use Case Example;70
5.3.4;3.4 Related Work;71
5.3.4.1;3.4.1 Rating Web Pages;71
5.3.4.2;3.4.2 Rating (Semi-)Structured Data;72
5.3.5;3.5 Conclusion;73
5.3.6;References;73
5.4;Chapter 4
Analyzing the Dynamics of Communication
in Online Social Networks
;75
5.4.1;4.1 Introduction;75
5.4.2;4.2 Characteristics of Online Communication;78
5.4.2.1;4.2.1 Background;78
5.4.2.2;4.2.2 Communication Modes in Social Networks;79
5.4.2.3;4.2.3 Prior Work on Communication Modalities;80
5.4.3;4.3 Rich Media Communication Patterns;81
5.4.3.1;4.3.1 Problem Formulation;84
5.4.3.1.1;4.3.1.1 Definitions;84
5.4.3.1.2;4.3.1.2 Data Model;84
5.4.3.1.3;4.3.1.3 Problem Statement;85
5.4.3.2;4.3.2 Conversational Themes;86
5.4.3.2.1;4.3.2.1 Chunk-Based Mixture Model of Themes;86
5.4.3.3;4.3.3 Interestingness;88
5.4.3.3.1;4.3.3.1 Interestingness of Participants;89
5.4.3.3.2;4.3.3.2 Interestingness of Conversations;90
5.4.3.3.3;4.3.3.3 Joint Optimization of Interestingness;91
5.4.3.4;4.3.4 Consequences of Interestingness;92
5.4.3.5;4.3.5 Experimental Studies;93
5.4.4;4.4 Information Diffusion;96
5.4.4.1;4.4.1 Preliminaries;97
5.4.4.1.1;4.4.1.1 Social Graph Model;97
5.4.4.1.2;4.4.1.2 Attribute Homophily;98
5.4.4.1.3;4.4.1.3 Topic Diffusion;98
5.4.4.1.4;4.4.1.4 Diffusion Series;99
5.4.4.2;4.4.2 Problem Statement;100
5.4.4.3;4.4.3 Characterizing Diffusion;101
5.4.4.4;4.4.4 Prediction Framework;102
5.4.4.5;4.4.5 Predicting Hidden States;104
5.4.4.6;4.4.6 Predicting Observed Action;104
5.4.4.7;4.4.7 Distortion Measurement;105
5.4.4.8;4.4.8 Experimental Studies;106
5.4.5;4.5 Summary and Future Work;107
5.4.6;References;108
5.5;Chapter 5
Qualitative Analysis of Commercial Social
Network Profiles
;111
5.5.1;5.1 Introduction;111
5.5.2;5.2 What Is a Commercial Social Network Profile?;112
5.5.2.1;5.2.1 Reciprocal Identification;113
5.5.2.2;5.2.2 Types of Commercial Social Network Profiles;114
5.5.2.2.1;5.2.2.1 Music;114
5.5.2.2.2;5.2.2.2 Films;114
5.5.2.2.3;5.2.2.3 Television;115
5.5.2.2.4;5.2.2.4 Public Figures;115
5.5.2.2.5;5.2.2.5 Events;115
5.5.3;5.3 Quantitative Analysis of CSNPs;116
5.5.3.1;5.3.1 Connections;116
5.5.3.2;5.3.2 Interactions;117
5.5.3.3;5.3.3 Hit Counters;118
5.5.3.4;5.3.4 Updates;118
5.5.4;5.4 Qualitative Analysis of CSNPs;118
5.5.4.1;5.4.1 Connections;119
5.5.4.1.1;5.4.1.1 Valid Connections;119
5.5.4.1.2;5.4.1.2 Connection Initiation;120
5.5.4.1.3;5.4.1.3 Connection Demographics;120
5.5.4.1.4;5.4.1.4 Friend Stacking;121
5.5.4.1.5;5.4.1.5 Div Overlaying;122
5.5.4.2;5.4.2 Interactions;122
5.5.4.2.1;5.4.2.1 Interaction Type, Source, and Content;122
5.5.4.2.2;5.4.2.2 Interaction Based Spam;124
5.5.4.2.3;5.4.2.3 Interaction Frequency and Timing;125
5.5.4.2.4;5.4.2.4 Interaction Uniqueness;127
5.5.5;5.5 Technical Notes;127
5.5.6;5.6 Summary;127
5.5.7;References;128
5.6;Chapter 6
Analysis of Social Networks Extracted
from Log Files
;130
5.6.1;6.1 Introduction;130
5.6.2;6.2 Social Networks;131
5.6.2.1;6.2.1 Social Network Analysis;132
5.6.2.2;6.2.2 Discovering Structure of Networks;132
5.6.2.2.1;6.2.2.1 Finding Communities in Social Networks;133
5.6.2.2.2;6.2.2.2 Finding Patterns in Social Networks;133
5.6.3;6.3 SNA from Log Files;134
5.6.3.1;6.3.1 Log File Analysis;134
5.6.4;6.4 Data Mining Methods Related to SNA and Log Mining;136
5.6.4.1;6.4.1 Clustering Techniques;137
5.6.4.1.1;6.4.1.1 Partitional Clustering;138
5.6.4.1.2;6.4.1.2 Hierarchical Clustering;140
5.6.4.2;6.4.2 Discovering of Network Evolution;143
5.6.4.3;6.4.3 Finding Overlapping Communities;144
5.6.5;6.5 Application of SNA;144
5.6.5.1;6.5.1 Web Mining;144
5.6.5.2;6.5.2 Phone Social Networks;145
5.6.5.3;6.5.3 Mail Logs, Server Logs;146
5.6.5.4;6.5.4 Business Sphere;146
5.6.5.5;6.5.5 Education;147
5.6.6;6.6 Case Study: Finding Students' Patterns of Behavior in LMS Moodle;149
5.6.6.1;6.6.1 Dataset Description;149
5.6.6.2;6.6.2 Experiment and Results;150
5.6.7;6.7 Conclusions;154
5.6.8;References;156
5.7;Chapter 7
Perspectives on Social Network Analysis
for Observational Scientific Data
;162
5.7.1;7.1 Introduction;162
5.7.2;7.2 Definitions and Background;163
5.7.2.1;7.2.1 Completeness;164
5.7.2.2;7.2.2 Certainty;164
5.7.2.3;7.2.3 Bias;166
5.7.3;7.3 Dolphin Societies;167
5.7.3.1;7.3.1 Shark Bay Data Collection;167
5.7.3.2;7.3.2 Fission Fussion Societies;168
5.7.3.3;7.3.3 Advantages and Disadvantages of Non-Human Studies;168
5.7.4;7.4 Completeness of Network-Sampling Subjects and Collecting Enough Data;169
5.7.4.1;7.4.1 Sampling Options;169
5.7.4.2;7.4.2 Sampling Methods Comparison;170
5.7.4.3;7.4.3 Amount of Data per Subject Necessary;171
5.7.4.4;7.4.4 Recommendations;172
5.7.5;7.5 Identifying Uncertainties and Biases;172
5.7.5.1;7.5.1 Uncertain Subjects and Behaviors;172
5.7.5.2;7.5.2 Observers Reliability and Consistency;173
5.7.5.3;7.5.3 Time and Behavioral Sampling;175
5.7.5.4;7.5.4 Depth and Association Sampling;175
5.7.5.5;7.5.5 Hidden Behaviors or Social Encounters;176
5.7.5.6;7.5.6 Observers Can Affect the Behaviors They Monitor;177
5.7.5.7;7.5.7 Recommendations;177
5.7.6;7.6 Computational Approaches to Improve Data Quality for Social Network Analysis;179
5.7.7;7.7 Final Thoughts;181
5.7.8;References;181
5.8;Chapter 8
Modeling Temporal Variation in Social Network:
An Evolutionary Web Graph Approach
;184
5.8.1;8.1 Introduction;184
5.8.1.1;8.1.1 Temporal Variation of a Social Network;186
5.8.2;8.2 Web as a Social Network;186
5.8.2.1;8.2.1 Concept of Web Graph;187
5.8.2.1.1;8.2.1.1 Definition;187
5.8.2.1.2;8.2.1.2 Properties;188
5.8.3;8.3 Evolution of Web Graph;190
5.8.4;8.4 Dynamic Web Graph Model;192
5.8.4.1;8.4.1 Dynamic Data Model Preliminaries;193
5.8.4.2;8.4.2 Temporal Structure-Based Schema;194
5.8.5;8.5 Conclusion and Future Works;197
5.8.6;References;198
5.9;9 Churn in Social Networks;200
5.9.1;9.1 Introduction;200
5.9.2;9.2 Understanding Churn in Social Networks;202
5.9.2.1;9.2.1 Reasons for Churn;203
5.9.2.2;9.2.2 Churn in Digital Social Networks;204
5.9.3;9.3 Definitions of Churn in Digital Social Networks;205
5.9.4;9.4 Empirical Analysis;209
5.9.4.1;9.4.1 Data Set 1: User Activity in a Discussion Board;209
5.9.4.2;9.4.2 Data Set 2: Activity in an Online Social Network;213
5.9.4.3;9.4.3 Summary;215
5.9.5;9.5 Models for Churn Prediction;216
5.9.5.1;9.5.1 Feature-Based Approaches;216
5.9.5.2;9.5.2 Social Network Analysis for Churn Prediction;218
5.9.6;9.6 Network Effects and Propagation of Churn;219
5.9.6.1;9.6.1 Network Views;219
5.9.6.2;9.6.2 Diffusion Models;220
5.9.6.3;9.6.3 Combining Feature-Based Approaches and Diffusion Models;223
5.9.7;9.7 Popularity and Influence in Social Networks;225
5.9.7.1;9.7.1 Social Roles and Influence in Discussion Boards;225
5.9.7.2;9.7.2 Popularity in Online Social Networks;227
5.9.8;9.8 Summary and Conclusion;231
5.9.9;References;232
6;Part II Social Media Mining and Search;236
6.1;Chapter 10
Discovering Mobile Social Networks
by Semantic Technologies
;237
6.1.1;10.1 Introduction;237
6.1.2;10.2 Contextual Dependency from Social Contexts;240
6.1.2.1;10.2.1 Network Separation: Divide;242
6.1.2.2;10.2.2 Network Superposition: Conquer;242
6.1.3;10.3 Social Network Ontology;243
6.1.3.1;10.3.1 Similarity-Based Ontology Alignment;243
6.1.3.2;10.3.2 Consensual Ontology Discovery;244
6.1.4;10.4 Interactive Discovery of Social Networks;246
6.1.5;10.5 Context-Based Service;250
6.1.6;10.6 Related Work and Discussion;251
6.1.7;10.7 Concluding Remarks and Future Work;251
6.1.8;References;252
6.2;Chapter 11
Online Identities and Social Networking
;254
6.2.1;11.1 Introduction;254
6.2.2;11.2 Background on Digital Identities;256
6.2.2.1;11.2.1 Civil vs. Digital Identities;256
6.2.2.2;11.2.2 The People Identification Problem;257
6.2.2.3;11.2.3 Requirements on Digital Identities;259
6.2.2.4;11.2.4 Classes of Digital Identities;262
6.2.2.5;11.2.5 Taxonomy of Approaches to Identities;263
6.2.3;11.3 Putting Social Relations to Work;264
6.2.3.1;11.3.1 Overview;264
6.2.3.2;11.3.2 User Authentication Using Social Relations;264
6.2.3.3;11.3.3 Connection Establishment Using Social Relations;266
6.2.3.4;11.3.4 Malware Propagation and Social Relations;266
6.2.4;11.4 Social Digital Identity;268
6.2.4.1;11.4.1 Overview;268
6.2.4.2;11.4.2 Generating a Seed Digital Identity;268
6.2.4.3;11.4.3 Binding a Person to a SDI Token;270
6.2.4.4;11.4.4 Example Deployment Scenario for SDI;272
6.2.4.5;11.4.5 Example Applications of SDI;273
6.2.5;11.5 Information and Threats in Social Networks;275
6.2.5.1;11.5.1 Information on Social Networks;275
6.2.5.2;11.5.2 Information for Establishing Identity;276
6.2.5.3;11.5.3 Identity vs. Privacy;277
6.2.6;11.6 Summary;278
6.2.7;References;278
6.3;Chapter 12
Detecting Communities in Social Networks
;281
6.3.1;12.1 Introduction;281
6.3.2;12.2 Definition of Community;282
6.3.2.1;12.2.1 Local definitions;282
6.3.2.2;12.2.2 Global definitions;282
6.3.2.3;12.2.3 Definitions Based on Vertex Similarity;283
6.3.3;12.3 Evaluating Communities;283
6.3.4;12.4 Methods for Community Detection;285
6.3.4.1;12.4.1 Divisive Algorithms;285
6.3.4.2;12.4.2 Modularity Optimization;285
6.3.4.3;12.4.3 Spectral Algorithms;286
6.3.4.4;12.4.4 Other Algorithms;286
6.3.5;12.5 Tools for Detecting Communities;286
6.3.5.1;12.5.1 Tools for Large-Scale Networks;287
6.3.5.2;12.5.2 Tools for Interactive Analysis;287
6.3.6;12.6 Conclusion;291
6.3.7;References;292
6.4;Chapter 13
Concept Discovery in Youtube.com
Using Factorization Method
;293
6.4.1;13.1 Introduction;293
6.4.2;13.2 Related Works;295
6.4.3;13.3 Public Attention Based Video Concept Discovery and Categorization for Video Searching;296
6.4.4;13.4 Dataset Collection;298
6.4.5;13.5 Data Pre-Processing;298
6.4.5.1;13.5.1 Data Cleaning;298
6.4.5.2;13.5.2 Text Matrix Generation;299
6.4.6;13.6 Video Processing via Clustering;299
6.4.6.1;13.6.1 Video Clustering and Concept Discovery;300
6.4.6.2;13.6.2 Factorized Component Entropy Measures for Vocabulary Construction;303
6.4.7;13.7 Experimental Evaluation;306
6.4.7.1;13.7.1 Empirical Setting;306
6.4.7.2;13.7.2 Video Categories and Concepts;307
6.4.7.3;13.7.3 User Comments vs. User Tags;310
6.4.8;13.8 Conclusion and Future Work;312
6.4.9;References;313
6.5;Chapter 14
Mining Regional Representative Photos
from Consumer-Generated Geotagged Photos
;315
6.5.1;14.1 Introduction;315
6.5.2;14.2 Related Work;316
6.5.3;14.3 Proposed Approach;318
6.5.3.1;14.3.1 Overview;318
6.5.3.2;14.3.2 Filtering Irrelevant Images;319
6.5.3.2.1;14.3.2.1 Image Representation;319
6.5.3.2.2;14.3.2.2 Visual Clustering;319
6.5.3.2.3;14.3.2.3 Selecting the Most Relevant Clusters;320
6.5.3.3;14.3.3 Detecting Representative Regions;320
6.5.3.4;14.3.4 Generating Representative Photographs;321
6.5.4;14.4 Experimental Results;321
6.5.4.1;14.4.1 Quantitative Evaluation;322
6.5.4.2;14.4.2 Examples of Regional Representative Photos;323
6.5.5;14.5 Conclusion and Future Work;327
6.5.6;References;327
6.6;Chapter 15
Collaborative Filtering Based on Choosing
a Different Number of Neighbors for Each User
;329
6.6.1;15.1 Introduction;329
6.6.2;15.2 Recommender Systems;330
6.6.3;15.3 Memory-Based Methods of Collaborative Filtering;331
6.6.4;15.4 Choosing Variable Number of Neighbors for Each User;333
6.6.4.1;15.4.1 Example;337
6.6.5;15.5 The Coverage Improvement;338
6.6.6;15.6 Evaluation of Our Techniques;340
6.6.7;15.7 Conclusions;341
6.6.8;References;341
6.7;Chapter 16
Discovering Communities from Social
Networks: Methodologies and Applications
;343
6.7.1;16.1 Introduction;343
6.7.2;16.2 Methodologies of Network Community Mining;344
6.7.2.1;16.2.1 Optimization Based Algorithms;344
6.7.2.2;16.2.2 Heuristic Methods ;347
6.7.2.3;16.2.3 Other Methods;349
6.7.3;16.3 Applications of Community Mining Algorithms;349
6.7.3.1;16.3.1 Network Reduction;350
6.7.3.2;16.3.2 Discovering Scientific Collaboration Groups from Social Networks;352
6.7.3.3;16.3.3 Mining Communities from Distributed and Dynamic Networks;355
6.7.4;16.4 Conclusions;356
6.7.5;References;356
7;Part III Social Network Infrastructures and Communities;359
7.1;Chapter 17
Decentralized Online Social Networks
;360
7.1.1;17.1 Introduction;360
7.1.1.1;17.1.1 Scope of the Chapter;362
7.1.2;17.2 Challenges for DOSN;362
7.1.2.1;17.2.1 Differences to Other Decentralized or P2P Applications;366
7.1.3;17.3 The Case for Decentralizing OSNs;367
7.1.4;17.4 General Purpose DOSNs;370
7.1.4.1;17.4.1 Proposed DOSN Approaches;371
7.1.5;17.5 Specialized Application Centric DOSNs;376
7.1.5.1;17.5.1 Social-Based P2P File Sharing;377
7.1.5.2;17.5.2 Shared Bookmarks and Collaborative Search;379
7.1.5.3;17.5.3 Micro-Blogging;379
7.1.6;17.6 Social Distributed Systems;380
7.1.6.1;17.6.1 Social DHT: SocialCircle;381
7.1.6.2;17.6.2 Storage/Back-up;383
7.1.7;17.7 Delay-Tolerant DOSN;384
7.1.8;17.8 Conclusion;386
7.1.9;References;387
7.2;Chapter 18
Multi-Relational Characterization of Dynamic
Social Network Communities
;390
7.2.1;18.1 Introduction;390
7.2.2;18.2 Actions, Networking and Community Formation;394
7.2.2.1;18.2.1 Mutual Awareness and Community Discovery;395
7.2.2.2;18.2.2 Extracting Communities Based on Mutual Awareness Structure;395
7.2.2.2.1;18.2.2.1 Computable Definition for Mutual Awareness;396
7.2.2.2.2;18.2.2.2 Mutual Awareness Expansion;397
7.2.2.3;18.2.3 Application: Query-Sensitive Community Extraction;400
7.2.3;18.3 Analyzing Communities and Evolutions in Dynamic Network;402
7.2.3.1;18.3.1 Sustained Membership, Evolution and Community Discovery;402
7.2.3.2;18.3.2 Extracting Sustained Evolving Communities;403
7.2.3.2.1;18.3.2.1 Problem Formulation;403
7.2.3.2.2;18.3.2.2 Extracting Communities and Evolutions;405
7.2.3.3;18.3.3 Application: Time-Dependent Ranking in Communities;406
7.2.4;18.4 Community Analysis on Multi-Relational Social Data;407
7.2.4.1;18.4.1 Embeddedness, Artifacts and Community Discovery;409
7.2.4.2;18.4.2 Extracting Communities from Rich-Context Social Networks;409
7.2.4.2.1;18.4.2.1 Problem Formulation;409
7.2.4.2.2;18.4.2.2 Metagraph Factorization;412
7.2.4.2.3;18.4.2.3 Time Evolving Extension;413
7.2.4.3;18.4.3 Application: Context-Sensitive Prediction in Enterprise;414
7.2.5;18.5 Conclusions and Future Directions;416
7.2.6;References;418
7.3;Chapter 19
Accessibility Testing of Social Websites
;420
7.3.1;19.1 Introduction;420
7.3.2;19.2 Social Websites and Their User Interfaces;422
7.3.2.1;19.2.1 Facebook Lite;422
7.3.3;19.3 WEB Accessibility Analysis;426
7.3.3.1;19.3.1 The Main Principles and Structure of WCAG 2.0;426
7.3.3.1.1;19.3.1.1 Structure of WCAG 2.0;426
7.3.3.1.2;19.3.1.2 Guideline 1. Perceivable;427
7.3.3.1.3;19.3.1.3 Guideline 2. Operable;427
7.3.3.1.4;19.3.1.4 Guideline 3. Understandable;427
7.3.3.1.5;19.3.1.5 Guideline 4. Robust;428
7.3.3.2;19.3.2 The XValid Software;428
7.3.4;19.4 Compare the Results with Other Website's Accessibility;434
7.3.5;19.5 Conclusions;435
7.3.6;References;436
7.4;Chapter 20
Understanding and Predicting Human Behavior
for Social Communities
;437
7.4.1;20.1 Introduction;437
7.4.2;20.2 User Data Management, Inference and Distribution;438
7.4.3;20.3 Enabling New Human Experiences;439
7.4.3.1;20.3.1 The Technologies;440
7.4.3.1.1;20.3.1.1 Social Networks;440
7.4.3.1.2;20.3.1.2 Reality Mining;440
7.4.3.1.3;20.3.1.3 Context-Awareness;440
7.4.3.2;20.3.2 Architectural Framework and Methodology;441
7.4.3.2.1;20.3.2.1 Data Management;441
7.4.3.2.2;20.3.2.2 Knowledge Generation;442
7.4.3.2.3;20.3.2.3 Service Exposure and Control;444
7.4.3.3;20.3.3 Innovations;444
7.4.4;20.4 The Social Enabler;445
7.4.4.1;20.4.1 The Algorithms;447
7.4.4.1.1;20.4.1.1 Distance;447
7.4.4.1.2;20.4.1.2 Similarity;447
7.4.4.1.3;20.4.1.3 Influence;448
7.4.4.1.4;20.4.1.4 Adjustments;448
7.4.4.2;20.4.2 Technological Considerations;450
7.4.5;20.5 Applications;450
7.4.5.1;20.5.1 The Augmented Social Experience;451
7.4.5.2;20.5.2 Future Self-Awareness;452
7.4.5.3;20.5.3 Advertising;453
7.4.6;20.6 Conclusions and Future Work;454
7.4.7;References;454
7.5;Chapter 21
Associating Human-Centered Concepts
with Social Networks Using Fuzzy Sets
;456
7.5.1;21.1 Introduction;456
7.5.2;21.2 An Introduction to Relational Network Theory;457
7.5.3;21.3 Computing with Words;460
7.5.4;21.4 On the Concept of Node Importance;461
7.5.5;21.5 Generalizing the Concept of a Cluster;463
7.5.6;21.6 Congested Nodes;466
7.5.7;21.7 Duration;471
7.5.8;21.8 Directed Graphs;471
7.5.9;21.9 Authority Figures;472
7.5.10;21.10 Conclusion;475
7.5.11;References;475
8;Part IV Privacy in Online Social Networks;477
8.1;Chapter 22
Managing Trust in Online Social Networks
;478
8.1.1;22.1 Introduction;478
8.1.2;22.2 Online Social Networks;480
8.1.3;22.3 Trust in Online Environment;482
8.1.4;22.4 Related Work;483
8.1.5;22.5 Trust Models Based on Subjective Logic;486
8.1.6;22.6 Trust Network Analysis;487
8.1.6.1;22.6.1 Operators for Deriving Trust;488
8.1.6.2;22.6.2 Trust Path Dependency and Network Simplification;489
8.1.7;22.7 Trust Transitivity Analysis;490
8.1.7.1;22.7.1 Uncertainty Favoring Trust Transitivity;490
8.1.7.2;22.7.2 Opposite Belief Favoring;491
8.1.7.3;22.7.3 Base Rate Sensitive Transitivity;492
8.1.7.4;22.7.4 Mass Hysteria;493
8.1.8;22.8 The Dirichlet Reputation System;494
8.1.9;22.9 Combining Trust and Reputation;497
8.1.10;22.10 Trust Derivation Based on Trust Comparisons;499
8.1.11;22.11 Conclusion;501
8.1.12;References;501
8.2;Chapter 23
Security and Privacy in Online Social Networks
;504
8.2.1;23.1 Introduction;504
8.2.1.1;23.1.1 Social Network Providers and Their Customers;506
8.2.1.2;23.1.2 Functional Overview of Online Social Networks;508
8.2.1.3;23.1.3 Modelling Data Contained in Online Social Networks;510
8.2.1.4;23.1.4 A Model for Social Network Services;513
8.2.2;23.2 Security Objectives: Privacy, Integrity, and Availability;513
8.2.2.1;23.2.1 Privacy;515
8.2.2.2;23.2.2 Integrity;516
8.2.2.3;23.2.3 Availability;516
8.2.3;23.3 Attack Spectrum and Countermeasures;517
8.2.3.1;23.3.1 Plain Impersonation;518
8.2.3.2;23.3.2 Profile Cloning;519
8.2.3.3;23.3.3 Profile Hijacking;519
8.2.3.4;23.3.4 Profile Porting;520
8.2.3.5;23.3.5 ID Theft;520
8.2.3.6;23.3.6 Profiling;521
8.2.3.7;23.3.7 Secondary Data Collection;522
8.2.3.8;23.3.8 Fake Requests;522
8.2.3.9;23.3.9 Crawling and Harvesting;523
8.2.3.10;23.3.10 Image Retrieval and Analysis;523
8.2.3.11;23.3.11 Communication Tracking;524
8.2.3.12;23.3.12 Fake Profiles and Sybil Attacks;524
8.2.3.13;23.3.13 Group Metamorphosis;525
8.2.3.14;23.3.14 Ballot Stuffing and Defamation;525
8.2.3.15;23.3.15 Censorship;526
8.2.3.16;23.3.16 Collusion Attacks;526
8.2.4;23.4 Summary and Conclusion;527
8.2.5;References;528
8.3;Chapter 24
Investigation of Key-Player Problem in Terrorist
Networks Using Bayes Conditional Probability
;530
8.3.1;24.1 Introduction;530
8.3.2;24.2 SNA Survey;533
8.3.3;24.3 SNA Measures;536
8.3.3.1;24.3.1 Degree;536
8.3.3.2;24.3.2 Betweenness;537
8.3.3.3;24.3.3 Closeness;538
8.3.4;24.4 Bayes Probability Theorem;538
8.3.5;24.5 Analysis & Results;540
8.3.6;24.6 Conclusion;550
8.3.7;References;552
8.4;Chapter 25
Optimizing Targeting of Intrusion Detection
Systems in Social Networks
;555
8.4.1;25.1 Introduction;555
8.4.2;25.2 Background;556
8.4.2.1;25.2.1 Epidemic Propagation in Social Networks;556
8.4.2.2;25.2.2 Centrality Indexes;557
8.4.2.2.1;25.2.2.1 Degree Centrality;557
8.4.2.2.2;25.2.2.2 Group Degree Centrality;558
8.4.2.2.3;25.2.2.3 Closeness Centrality;559
8.4.2.2.4;25.2.2.4 Group Closeness Centrality;559
8.4.2.2.5;25.2.2.5 Betweenness Centrality;559
8.4.2.2.6;25.2.2.6 Group Betweenness Centrality;560
8.4.2.2.7;25.2.2.7 Random Walk Betweenness Centrality;560
8.4.3;25.3 Experimental Setup;561
8.4.3.1;25.3.1 Extracting the Social Network;561
8.4.3.2;25.3.2 Pinpointing Central Users;562
8.4.3.3;25.3.3 Simulation;563
8.4.4;25.4 Experiment Results;564
8.4.4.1;25.4.1 Threat Prevalence;564
8.4.4.2;25.4.2 Epidemic Half-Life;566
8.4.4.3;25.4.3 Detection Time;567
8.4.4.4;25.4.4 Intercepted Threats;567
8.4.4.5;25.4.5 IDS Effectiveness;569
8.4.5;25.5 Summary and Conclusions;572
8.4.6;References;573
8.5;Chapter 26
Security Requirements for Social Networks
inWeb 2.0
;575
8.5.1;26.1 Introduction;575
8.5.2;26.2 Context, Threats, and Incidents;577
8.5.3;26.3 Two Patterns;578
8.5.3.1;26.3.1 Participation-Collaboration;578
8.5.3.1.1;26.3.1.1 Intent;578
8.5.3.1.2;26.3.1.2 Example;579
8.5.3.1.3;26.3.1.3 Context;579
8.5.3.1.4;26.3.1.4 Problem;579
8.5.3.1.5;26.3.1.5 Solution;579
8.5.3.1.6;26.3.1.6 Dynamics;580
8.5.3.1.7;26.3.1.7 Implementation;581
8.5.3.1.8;26.3.1.8 Known Uses;581
8.5.3.1.9;26.3.1.9 Related Patterns;582
8.5.3.1.10;26.3.1.10 Consequences;582
8.5.3.2;26.3.2 Collaborative Tagging;582
8.5.3.2.1;26.3.2.1 Intent;582
8.5.3.2.2;26.3.2.2 Example;582
8.5.3.2.3;26.3.2.3 Context;582
8.5.3.2.4;26.3.2.4 Problem;583
8.5.3.2.5;26.3.2.5 Solution;583
8.5.3.2.6;26.3.2.6 Known Uses;585
8.5.3.2.7;26.3.2.7 Consequences;585
8.5.4;26.4 Improvements;585
8.5.5;26.5 Conclusions;586
8.5.6;References;587
9;Part V Visualisation and Applications of Social Networks;589
9.1;Chapter 27
Visualization of Social Networks
;590
9.1.1;27.1 Introduction;590
9.1.2;27.2 Social Network Analysis;592
9.1.2.1;27.2.1 Graph Theory;592
9.1.2.2;27.2.2 Centrality;593
9.1.2.3;27.2.3 Clustering;594
9.1.3;27.3 Visualization;595
9.1.3.1;27.3.1 Node-Edge Diagrams;595
9.1.3.1.1;27.3.1.1 Random Layout;595
9.1.3.1.2;27.3.1.2 Force-Directed Layout;596
9.1.3.1.3;27.3.1.3 Tree Layout;596
9.1.3.2;27.3.2 Matrix Representations;598
9.1.4;27.4 Visualizing Online Social Networks;599
9.1.4.1;27.4.1 Web Communities;599
9.1.4.2;27.4.2 Email Groups;602
9.1.4.3;27.4.3 Digital Libraries;604
9.1.4.3.1;27.4.3.1 Co-Authorship Networks;604
9.1.4.3.2;27.4.3.2 Co-Citation Relations;606
9.1.4.4;27.4.4 Web 2.0 Services;609
9.1.4.5;27.4.5 Summary;612
9.1.5;27.5 Conclusions;613
9.1.6;References;614
9.2;Chapter 28
Novel Visualizations and Interactions for Social
Networks Exploration
;616
9.2.1;28.1 Introduction;616
9.2.2;28.2 Node-Link Diagrams;617
9.2.3;28.3 Scaling to Larger Networks;619
9.2.3.1;28.3.1 Reducing the Quantity of Information;619
9.2.3.2;28.3.2 Incremental Exploration;621
9.2.3.3;28.3.3 Using More Visual Space;622
9.2.3.4;28.3.4 Alternative Representations;623
9.2.4;28.4 Adjacency Matrix Representations;624
9.2.4.1;28.4.1 Reordering;625
9.2.4.2;28.4.2 Navigation;626
9.2.5;28.5 Visualizing Social Networks with Matrix-Based Representations;627
9.2.5.1;28.5.1 Matrix or Node-Link Diagram?;627
9.2.5.2;28.5.2 Matrix + Node-Link Diagrams;628
9.2.5.2.1;28.5.2.1 Initiate Exploration;629
9.2.5.2.2;28.5.2.2 Explore Interactively;630
9.2.5.2.3;28.5.2.3 Find a Consensus in the Data;631
9.2.5.2.4;28.5.2.4 Present Findings;632
9.2.5.3;28.5.3 Hybrid Representations;632
9.2.5.3.1;28.5.3.1 Augmenting Matrices;632
9.2.5.3.2;28.5.3.2 Merging Matrix and Node-Link Diagram;635
9.2.6;28.6 Conclusion;637
9.2.7;References;639
9.3;Chapter 29
Applications of Social Network Analysis
;642
9.3.1;29.1 Introduction;642
9.3.2;29.2 Social Network Analysis;643
9.3.3;29.3 Applications of Social Network Analysis;644
9.3.3.1;29.3.1 Organizational Issues ;645
9.3.3.2;29.3.2 Recommendation and E-commerce Systems ;647
9.3.3.3;29.3.3 Covert Networks;648
9.3.3.4;29.3.4 Web Applications;649
9.3.3.5;29.3.5 Community Welfare;650
9.3.3.6;29.3.6 Collaboration Networks;651
9.3.3.7;29.3.7 Co-Citation Networks ;652
9.3.4;29.4 Conclusion;653
9.3.5;References;653
9.4;Chapter 30
Online Advertising in Social Networks
;655
9.4.1;30.1 Introduction;655
9.4.1.1;30.1.1 Online Advertising;656
9.4.2;30.2 Identifying the Social Network Effect in Online Advertising;658
9.4.2.1;30.2.1 Homophily;658
9.4.2.1.1;30.2.1.1 Similarity Between Friends;659
9.4.2.1.2;30.2.1.2 Are Similar Users Friends?;659
9.4.2.2;30.2.2 Influencers;660
9.4.2.2.1;30.2.2.1 Modeling the Spread of Influence ;660
9.4.2.2.2;30.2.2.2 Leveraging Rich Data for Social Network Based Marketing;662
9.4.3;30.3 Online Ad Targeting;662
9.4.3.1;30.3.1 Targeting Based on User Information;664
9.4.3.1.1;30.3.1.1 Contextual Targeting;664
9.4.3.1.2;30.3.1.2 User Segment Targeting;665
9.4.3.1.3;30.3.1.3 Behavioral Targeting ;665
9.4.3.2;30.3.2 Social Network Targeting;666
9.4.3.2.1;30.3.2.1 Using Peer-Pressure for Targeting;666
9.4.3.2.2;30.3.2.2 Using Friends for Targeting;666
9.4.3.2.3;30.3.2.3 Using Social Features for Targeting ;667
9.4.3.2.4;30.3.2.4 Targeting in Social Neighborhoods;668
9.4.3.3;30.3.3 Combining User Features and Social Network Features;668
9.4.3.3.1;30.3.3.1 Weighted Combination of Scores ;669
9.4.3.3.2;30.3.3.2 Ensemble Classifier;669
9.4.4;30.4 Applications of Social Network Advertising;670
9.4.4.1;30.4.1 Yahoo! Instant Messenger Social Network;671
9.4.4.1.1;30.4.1.1 Yahoo! IM Graph Statistics;671
9.4.4.1.2;30.4.1.2 Conversations in the IM Social Network;672
9.4.4.2;30.4.2 Predicting Ad Clicks;673
9.4.4.2.1;30.4.2.1 Dataset Description;674
9.4.4.2.2;30.4.2.2 Measuring the Social Network Effect in Ad Clicks;674
9.4.4.2.3;30.4.2.3 Modeling Propensity to Click on Ads;676
9.4.4.3;30.4.3 Predicting Product Adoption in Social Networks;678
9.4.4.3.1;30.4.3.1 Dataset Description;679
9.4.4.3.2;30.4.3.2 Measuring the Social Network Effect in Product Adoption;681
9.4.4.3.3;30.4.3.3 Modeling the Propensity to Adopt the PC to Phone Product;687
9.4.5;30.5 Conclusion;690
9.4.6;References;691
9.5;Chapter 31
Social Bookmarking on a Company’s Intranet:
A Study of Technology Adoption and Diffusion
;694
9.5.1;31.1 Introduction;694
9.5.2;31.2 Review of Literature;696
9.5.3;31.3 The Study;699
9.5.3.1;31.3.1 Overview of the Research Center;699
9.5.3.2;31.3.2 Overview of the Social Bookmarking Tool (SBT);700
9.5.3.3;31.3.3 Data Collection and Analysis;702
9.5.4;31.4 Discussion;708
9.5.5;References;712
10;Index;715