From Sociology to Computing in Social Networks (eBook)

Theory, Foundations and Applications

Nasrullah Memon, Reda Alhajj (Herausgeber)

eBook Download: PDF

2010
430 Seiten
Springer Wien (Verlag)
9783709102947 (ISBN)

Infos

Important aspects of social networking analysis are covered in this work by combining experimental and theoretical research. A specific focus is devoted to emerging trends and the industry needs associated with utilizing data mining techniques. Some of the techniques covered include data mining advances in the discovery and analysis of communities, in the personalization of solitary activities (like searches) and social activities (like discovering potential friends), in the analysis of user behavior in open fora (like conventional sites, blogs and fora) and in commercial platforms (like e-auctions), and in the associated security and privacy-preservation challenges; as well as social network modeling, scalable, customizable social network infrastructure construction, and the identification and discovery of dynamic growth and evolution patterns using machine learning approaches or multi-agent based simulation. These topics will be of interest to practitioners and researchers alike in this dynamic and growing field.

Inhalt

Title Page 
3 
Copyright Page 
4 
Table of Contents 
5 
List of Contributors 
15 
Social Networks: A Powerful Model for Serving a Wide Range of Domains 20
1 General Overview 20
2 The need for the Lecture Notes in Social Network Series 21
3 Organization of the Volume 
22 
Section 1: Mining-based Social Network Methods 
22 
Section 2: Dynamics in Soial Network Models 
24 
Section 3: Discovering Structures in Social Networks 
24 
Section 4: Social Media 
25 
Section 5: Software Applications 
26 
Acknowledgements 27
References 27
Part 1 Mining-based Social Network Methods 
29 
Employing Social Network Construction and Analysis in Web Structure Optimization 30
1 Introduction 
31 
2 Related Work 
33 
3 The Proposed Website Analysis Approach 
35 
3.1 Web Structure Mining 
37 
3.1.1 Weighted PageRank Based Ranking 37
3.1.2 HITS Based Ranking 
39 
3.1.3 Social Network Based Ranking 
39 
3.2 Ranking Pages Based on Web Log Mining 
40 
3.2.1 Preprocessing: 
40 
3.2.2 Computing Log Rank Values 
41 
3.3 Analyzing the Outcome from the First Phase 
42 
3.3.1 Preprocessing 
42 
3.3.2 Guideline to Recommendations 
43 
3.4 Ranking Pages by Employing Web Content Mining 44
3.5 The Relinking Process 
45 
4 Evaluation of the Proposed Approach 
46 
5 Summary and Conlusion 49
References 
50 
Mining Heterogeneous Social Networks for Egocentric Information Abstraction 52
1 Introduction 52
2 Related Works 
56 
3 Methodology 
57 
3.1 Ego-based Feature Extraction 
58 
3.2 Nodes and Paths Sampling 
59 
3.3 Information Distilling 
60 
3.4 Abstracted Graph Construction 
63 
4 Evaluations 
63 
4.1 Case Study for a Movie Network 
64 
4.2 Human Study for Crime Identification 
69 
5 Discussions 
73 
6 Conclusions 
73 
References 
74 
PROG: A Complementary Model to the Social Networks for Mining Forums 76
1 Introduction 
76 
2 Background 
77 
3 Post-reply Opinion Graph 
79 
3.1 Properties 
81 
3.2 Components 
81 
4 Measures 84
4.1 Structure-oriented Measures 
84 
4.2 Opinion-oriented Measures 
85 
4.3 Time-oriented Measures 
87 
4.4 Topic-oriented Measures 
88 
5 Application 
90 
6 Conclusion and Future Work 
95 
References 
96 
Socio-contextual Network Mining for User Assistance in Web-based Knowledge Gathering Tasks 
97 
1 Introduction 
97 
2 Related Work 
99 
3 A Socio-contextual Approach to Contextual User Assistance in WKG 
100 
3.1 Challenges in WKG 
100 
3.2 Observing a WKG Task 
100 
3.3 Semantic Link Network 
101 
3.4 Types of SLN 
101 
3.5 Construction of SLN 
102 
3.6 Restructuring Master SLN 
104 
3.7 Socio-contextual Mining of Master SLN for Discovering Similar Structures 
104 
4 Experiments and Evaluation 
105 
4.1 Experiment 
106 
4.2 Analysis 
107 
5 Conclusion 
108 
Acknowledgements 
108 
References 
109 
Integrating Entropy and Closed Freqent Pattern Mining for Social Netword Modelling and Analysis 
110 
1 Introduction 111
2 Related Works 
114 
3 Problem Staement 
116 
4 The Proposed Framework 
116 
4.1 Feature Extraction Model 
117 
4.2 Social Network Creation Model 
119 
4.3 Statistical Analysis Model 119
4.4 Visualization Model 
119 
5 Dynamic Behavior Analysis 
120 
6 Experimental Results 
122 
6.1 Synthetic Dataset 
122 
6.2 Enron E-mail Dataset 
123 
6.2.1 Data Processing and Network Extraction 
124 
6.2.2 Dynamic User Behavior 
125 
7 Conclusion and Future Work 
128 
References 
128 
Part 2 Dynamics in Social Network Models 
131 
Visualisation of the Dynamics for Computer-mediated Social Networks-concept and Exemplary Cases 
132 
1 Introduction 
133 
2 Data Collection and Processing 
134 
3 Visualisation Approaches - Towards a Representation of Network Dynamics 
136 
4 Implementation 
138 
5 Example Cases for the Visualisation Method 
139 
5.1 CSCL Citation Network 
139 
5.2 OpenSimulator Network 
141 
6 Conclusion and Perspectives 
146 
References 
147 
EWAS: Modeling Application for Early Detection of Terrorist Threats 
148 
1 Introduction 
148 
2 State-of-the-Art 151
3 Problems with Existing Systems 
152 
4 Functional Requirements 153
5 The Proposed System 
154 
6 Data Processing Phases 
155 
6.1 Acquisition Phase 157
6.2 Extration Phase 
157 
6.3 Information Generation Phase 157
6.4 Investigating Phase 
157 
6.5 Warining Generation Phase 159
7 EWAS System Architecture 
159 
7.1 Acquisition Cluster 159
7.2 Extraction Cluster 
160 
7.3 Investigation System 
161 
7.4 Warning Generation System 
161 
7.5 Warning Generation Rule Anatomy 
162 
8 Testing and Implementation Strategy for EWAS 
164 
9 Compliance with Requirements 
165 
10 Experimental Results 
165 
11 Conclusion and Future Extensions 
166 
References 
168 
Complex Dynamics in Information Sharing Networks 
170 
1 Introduction 
170 
2 Literature 
171 
3 Methods and Data 
174 
4 Results 
175 
4.1 Usage Trends 
176 
4.2 Fourier Analysis 
179 
4.3 Usage Distribution Analysis 
181 
4.4 Social Network Analysis 
182 
4.5 Regession Analysis 
185 
5 Discussion and Conclusion 
187 
References 188
Harnessing Wisdom of the Crowds Dynamics for Time-dependent Reputation and Ranking 
190 
1 Introduction 
190 
2 Social and Term Ranking Based on Reputation 
192 
2.1 User Reputation 
192 
2.2 Document Reputation 
193 
2.3 Time Dynamics 
193 
2.4 Reputation Ranking: Combining Document and User Reputation 
194 
2.5 Term-reputation Ranking: Combining Reputation Ranking and Term Ranking 
194 
3 Experiment Results 
195 
3.1 Expermental Setup 
195 
3.2 Documents 
197 
3.3 Ranking Popular Documents 
198 
3.4 Ranking Less Popular Documents 
200 
3.5 Ranking Popular Documents Using Term-reputation 
202 
4 Related Work 
204 
5 Conclusion 
205 
References 
206 
Part 3 Discovering Structures in Social Networks 
208 
Detecting Communities in Social Networks Using Local Information 
209 
1 Introduction 
209 
2 Related Work 
211 
3 Preliminaries 
213 
3.1 Problem Definition 
213 
3.2 Previous Approaches 
214 
4 Our Approach 
215 
4.1 The Local Community Metric L 
216 
4.2 Local Community Struture Discovery 
217 
4.3 Iterative Local Expansion 
219 
5 Experiment Results 
220 
5.1 Comparing Metric Accuracy 
220 
5.1.1 The NCAA Football Network 
220 
5.1.2 The Amazon Co-purchase Network 
221 
5.2 Interatively Finding Overlapping Communities 
224 
6 Conclusion and Future Work 
224 
7 Acknowledgments 
225 
References 
225 
Why Do Diffusion Data Not Fit the Logistic Model? A Note on Network Discreteness, Heterogeneity and Anisotropy 
227 
1 A Brief Historical Sketch 
228 
2 The Logistic Function 
229 
3 Empirical Data 
231 
4 Accounting for Anomalies 
232 
5 Net Discreteness 
232 
6 Net Heterogeneity 
233 
7 Net Anisotropy 
235 
8 A Test of DHA Indices 
237 
9 Conclusion 
239 
References 
239 
Interlocking Communication Measuring Collaborative Intensity in Social Networks 
243 
1 Introduction 
243 
2 Research on Collaboration Networks 
244 
3 From Communication to Collaboration 
245 
4 Collaborative Intensity 
247 
5 Case Studies 
248 
6 Evaluating Collaborative Intensity 
251 
7 Filtering 
255 
8 Centrality in Weighted Networks 
259 
9 Conclusion 
262 
References 
263 
The Strucural Underpinnings of Policy Learning: A Classroom Policy Simulation 
265 
1 Learning Mechanisms 
268 
2 Hypothese 
270 
3 Research Design 
270 
4 Findings 
274 
5 Diffusion versus Interaction 
278 
6 Tie Intensity and Learning 
279 
7 Discussion 
282 
8 Conclusion 
285 
References 
286 
Part 4 Social Media 
290 
A Journey to the Core of the Blogosphere 
291 
1 Introduction 
291 
1.1 The Blogosphere 
291 
1.2 Rationale 
292 
2 Data Set Acquisition 
293 
2.1 Blog Seeds 
293 
Untitled 294
2.2 Using the Blogroll 
294 
2.3 Crawling Blogroll Links 
294 
2.4 Crawling the Data Sets 
295 
3 Core Model 
296 
3.1 Notations 
296 
3.2 Existing Core Models 
296 
3.3 Core Models for Directed Graphs 
297 
3.4 The In-core Algorithm 
298 
4 Core Analysis 
298 
4.1 Comparison to Random Networks 
298 
4.2 Comparing the Data Sets 
302 
4.3 Comparison with the Core/Periphery Model 304
5 Identifying A-List Blogs 
304 
5.1 Constraints 
304 
5.2 Structural Analysis 
305 
5.3 Core Independency 
305 
6 Conclusion 
309 
References 
310 
Social Physics of the Blogosphere Capturing, Analyzing and Presenting Interdependencies within a Single Framework 
311 
1 Introduction 
312 
1.1 The Bigger Picture 
312 
1.2 Research Rationale 313
1.3 Research Overview and Chapter Arrangement 
314 
2 Related Work 
314 
3 Framework 
316 
4 Extraction: Data Elements and Crawler Implementation 
318 
4.1 Information Elements 
318 
4.2 Crawler Action-Sequence 
320 
4.3 Recognizing Weblogs 
321 
4.4 Recognizing Feeds 
322 
4.5 Storing Crawled Data 
322 
4.6 Refreshing Period of Crawled Data 
323 
5 Analysis: Integration of Proprietary and Existing Research Efforts 
324 
5.1 Network Analysis 
324 
5.2 Content Analysis 
326 
6 Visualization 
327 
7 Conclusion 
328 
References 
329 
Twitmographics: Learning the Emergent Properties of the Twitter Community 
332 
1 Introduction 
332 
2 Prior Work 
333 
3 Proposed Framework 
336 
3.1 Features of MessageStats 
336 
3.2 Features of UserDemographics 
338 
3.3 Message Harvesting Process 
340 
4 Validation Results on Synthesized Attributes 
341 
5 Case Studies 
344 
5.1 Case 1: "Iran Election" 
345 
5.2 Case 2: "iPhone" 
346 
5.3 Case 3: "Obama" 348
6 Conclusion 
349 
References 
350 
Dissecting Twitter: A Review on Current Microblogging Research and Lessons from Related Fields 352
1 Introduction 
352 
2 Exploratory Studies on Twitter 
354 
2.1 Twitter's Properties and Emergent Features 
354 
2.2 Message Addressivety and Forwarding on Twitter 
356 
3 Information Spread and Self-organization on Twitter and Related Disciplines 
357 
3.1 Twitter in Crisis and Convergence 
357 
3.2 Pattern Detection and User/Message Clustering Twitter 
359 
3.3 Related Literature: On Viral InFormation Spread and Memetics 
360 
3.4 Related Literature: Approaches to Trend Analysis from Existing Blogs and Social Media 
361 
4 Twitter for Sentiment and Opinion Analysis 
362 
4.1 Twitter to Gauge User Interest 
362 
4.2 Twitter for Opinion Analysis: Case Study in Political Debates 
362 
4.3 Twitter for Marketing and Brand Setiment Analysis 
363 
5 Human Factors on Twitter 
364 
5.1 User Intention for Participating in Twitter 
364 
5.2 Best Practices and Typical Usage Scenarios 
365 
5.3 Social Information Needs and Wants 
365 
6 Twitter in Computer-based Visualizations 365
6.1 Twitter: Visualization and CHI studies 
366 
6.2 Twitter Web-based Visualization Tools 
367 
7 Comparison 
369 
8 Conclusion 
369 
References 
370 
Part 5 Software Applications 
372 
Unleash the CSS-Factor A Social Capital Approach to the Benefits and Challenges of Corporate Social Software 
373 
1 Introduction 
374 
2 The intranet (R)Evolution 
375 
3 E=MC2 
376 
4 Corporate Social Software - A secret Weapon? 
377 
5 Benefits versus Challenges of CSS-Evidence from Inside the Firewall 
379 
6 Conclusion 
381 
References 
383 
Extending SQL to Support Privacy Policies 
385 
1 Introduction 
386 
1.1 Requirements for Extension 
386 
1.2 User Privacy Requirements 
387 
1.3 Organization 
387 
2 Extending SQL to Support Privacy Policies 
388 
2.1 Overview of CREATE TABLE 
388 
2.2 Extended CREATE TABLE 
388 
2.3 Overview of GRANT 
389 
2.4 Modified GRANT 
390 
2.5 Overview of REVOKE 
391 
2.6 Modified REVOKE 
392 
3 Model Semantics 
393 
3.1 Privacy Catalogues 
393 
3.2 Extended Data Manipulation Language 
394 
4 Complexity Analysis 
396 
4.1 Implenentation 
397 
5 Related Work 
399 
6 Conclusion 
400 
References 
400 
nCompass Service Oriented Architecture for Tacit Collaboration Services 
402 
1 Introduction 
402 
2 Objectives 
403 
3 Technical Foundations 
404 
3.1 Oculus nSpace 
404 
3.2 Service Oriented Archiecture (SOA) 
404 
3.3 Tacit Collaboration Approach 
405 
4 Scenario Illustrating Use and Impact 
406 
5 nCompass 
407 
6 nCompass Core Services 
409 
6.1 Analysis Log Service (ALS) 
409 
6.1.1 Analysis Log Events (ALEs) 
409 
6.2 Content Management Service (CMS) 
410 
6.3 Authention Management Service (AMS) 
412 
6.4 Group Management Service (GMS) 
413 
7 Experiments and Results 
414 
7.1 Ease of Integration 
414 
7.2 Impact on Experiment Design 
415 
7.3 Tacit Collaboration Through Context-sharing 
416 
8 Related Work 
417 
9 Conclusion and Future Work 
418 
Acknowledgments 
418 
References 
419 
SOA Security Aspects in Web-based Architectural Design 
421 
1 Introduction 
422 
1.1 CRUD Operations 
423 
1.2 Security Using SOA 
424 
1.3 Security Problems 
424 
2 WS-Security in CRUD operations 
425 
3 Proposed Architecture 
425 
3.1 Interface between Nurse and Application 
425 
3.2 Interface betwrrn Doctor and Application 
426 
3.3 Swcurity Measures for an Intruder 
426 
3.4 Security Implementation at Nurse's End 
426 
3.5 Security Implementation at the Doctor's End 
427 
3.6 Prevention from Replay Attack 
430 
4 Experiments and Results 
431 
4.1 Security Scenariors 
432 
5 Related Work 
433 
6 Conclusion and Future Work 
435 
References 
435 

Produkt-Details

• Erscheint lt. Verlag: 26.8.2010
• Reihe/Serie: Lecture Notes in Social Networks
• Reihe/Serie: Lecture Notes in Social Networks
• Zusatzinfo: XIX, 430 p.
• Verlagsort: Vienna
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Web / Internet
• Themenwelt: Naturwissenschaften ► Physik / Astronomie
• Themenwelt: Technik
• Schlagworte: Calculus • Computer • Data Mining • machine learning • Modeling • multiagen based simulation • Optimization • pattern mining • Social Networking • Visualization • Web communities
• ISBN-13: 9783709102947 / 9783709102947