Data Mining for Social Network Data

edited by Nasrullah Memon, Jennifer Jie Xu, David L. Hicks, Hsinchun Chen

Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics, Medical Informatics, Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.

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Year of publication:	2010
Authors:	Memon, Nasrullah
Other Persons:	Xu, Jennifer Jie (contributor) ; Hicks, David L. (contributor) ; Chen, Hsinchun (contributor)
Publisher:	Boston, MA : Springer Science+Business Media, LLC
Subject:	Data Mining \| Data mining \| Soziales Netzwerk \| Social network \| Social Web \| Social web
Description of contents:	Table of Contents [external.dandelon.com]

Extent:	Online-Ressource v.: digital
Series:	Annals of Information Systems ; 12
Type of publication:	Book / Working Paper
Language:	English
Notes:	Includes bibliographical references Contents; Contributors; 1 Social Network Data Mining: Research Questions, Techniques, and Applications; 1.1 Introduction; 1.2 Network Mining: Research Questions; 1.2.1 Static Structure Mining; 1.2.2 Dynamic Structure Mining; 1.3 Network Mining: Techniques and Applications; 1.4 Conclusions and Future Directions; References; 2 Automatic Expansion of a Social Network UsingSentiment Analysis; 2.1 Introduction; 2.2 An Algorithm for Expanding a Signed Social Network of Attitudes; 2.2.1 Signed Social Network; 2.2.2 Quotation Network; 2.2.3 Automatic Expansion of the Signed Social Network 2.3 Filtering the Results Using Output Network Structural Properties2.4 Data, Experiments, and Evaluation; 2.4.1 The News Data; 2.4.2 The Social Networks Used as Input; 2.4.3 Evaluation Criteria; 2.4.4 Experiments and Evaluation; 2.5 Related Work; 2.6 Conclusions and Future Work; References; 3 Automatic Mapping of Social Networks of Actors from Text Corpora: Time Series Analysis; 3.1 Introduction; 3.2 Methods; 3.2.1 Link Coding with Proximities not ''Bag of Words''; 3.2.2 Optimal Window Size for Actor Social Networks; 3.2.3 Actor Co-occurrence Segmentation Software 3.2.4 Network Centrality Measures3.2.5 Time Segmentation; 3.2.6 Creating the String Replacement and Include Lists; 3.2.7 Post-Processing of Link Data for Centrality Measures; 3.2.8 Time Series Statistical Analysis; 3.2.9 Combining Visualization with Statistical Centrality of Actors; 3.3 Results; 3.3.1 Hypotheses Tests; 3.4 Discussion; References; 4 A Social Network-Based Recommender System (SNRS); 4.1 Introduction; 4.2 Background; 4.3 A Social Network-Based Recommender System; 4.3.1 Immediate Friend Inference; 4.3.1.1 User Preference; 4.3.1.2 Item Acceptance 4.3.1.3 Influence from Immediate Friends4.3.2 Distant Friend Inference; 4.4 Data set; 4.4.1 Review Correlations of Immediate Friends; 4.4.2 Rating Correlations of Immediate Friends; 4.5 Experiments; 4.5.1 Comparison Methods; 4.5.2 Prediction Accuracy And Coverage; 4.5.3 Data Sparsity; 4.5.4 Cold-Start; 4.5.5 Role of Distant Friends; 4.6 Semantic Filtering of Social Networks; 4.7 Related Work; 4.8 Conclusions; References; 5 Network Analysis of US Air Transportation Network; 5.1 Introduction; 5.2 Network Analysis Foundation; 5.2.1 Network Foundation; 5.2.2 Network Properties 5.2.2.1 Average Shortest Path (Distance)5.2.2.2 Degree; 5.2.2.3 Betweeness; 5.2.2.4 Clustering Coefficient; 5.3 The US Air Transportation Network Analysis; 5.3.1 The US Air Transportation Network Data; 5.3.2 Network Topological Properties; 5.3.3 9/11 Impact on the Aviation Industry; 5.4 Network Dynamics; 5.4.1 Network Modeling; 5.4.2 Network Aging Effect; 5.5 Conclusion and Discussion; References; 6 Identifying High-Status Nodes in Knowledge Networks; 6.1 Introduction; 6.2 Literature Review; 6.2.1 Social Network Measures; 6.2.2 Innovation and Knowledge Networks 6.3 Research Design and Testbed Electronic reproduction; Available via World Wide Web
ISBN:	978-1-4419-6287-4 ; 978-1-4419-6286-7
Other identifiers:	10.1007/978-1-4419-6287-4 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

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