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The Influence of Technology on Social Network Analysis and Mining

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Cover of 'The Influence of Technology on Social Network Analysis and Mining'

Table of Contents

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    Book Overview
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    Chapter 1 EgoClustering: Overlapping Community Detection via Merged Friendship-Groups
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    Chapter 2 Optimization Techniques for Multiple Centrality Computations
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    Chapter 3 Application of Social Network Metrics to a Trust-Aware Collaborative Model for Generating Personalized User Recommendations
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    Chapter 4 TweCoM: Topic and Context Mining from Twitter
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    Chapter 5 Pixel-Oriented Network Visualization: Static Visualization of Change in Social Networks
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    Chapter 6 Building Expert Recommenders from Email-Based Personal Social Networks
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    Chapter 7 A Local Structure-Based Method for Nodes Clustering: Application to a Large Mobile Phone Social Network
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    Chapter 8 Virus Propagation Modeling in Facebook
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    Chapter 9 Comparing and Visualizing the Social Spreading of Products on a Large Social Network
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    Chapter 10 Engagingness and Responsiveness Behavior Models on the Enron Email Network and Its Application to Email Reply Order Prediction
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    Chapter 11 Efficient Extraction of High-Betweenness Vertices from Heterogeneous Networks
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    Chapter 12 Cross-Domain Analysis of the Blogosphere for Trend Prediction
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    Chapter 13 Informative Value of Individual and Relational Data Compared Through Business-Oriented Community Detection
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    Chapter 14 Clustering Social Networks Using Distance-Preserving Subgraphs
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    Chapter 15 Extraction of Spatio-Temporal Data for Social Networks
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    Chapter 16 Detecting Communities in Massive Networks Efficiently with Flexible Resolution
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    Chapter 17 Detecting Emergent Behavior in a Social Network of Agents
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    Chapter 18 Factors Enabling Information Propagation in a Social Network Site
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    Chapter 19 Learning from the Past: An Analysis of Person Name Corrections in the DBLP Collection and Social Network Properties of Affected Entities
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    Chapter 20 Towards Leader Based Recommendations
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    Chapter 21 Enhancing Child Safety in MMOGs
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    Chapter 22 An Adaptive Framework for Discovery and Mining of User Profiles from Social Web-Based Interest Communities
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    Chapter 23 DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases
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    Chapter 24 Extending Social Network Analysis with Discourse Analysis: Combining Relational with Interpretive Data
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    Chapter 25 How Latent Class Models Matter to Social Network Analysis and Mining: Exploring the Emergence of Community
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    Chapter 26 Integrating Online Social Network Analysis in Personalized Web Search
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    Chapter 27 Evolution of Online Forum Communities
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    Chapter 28 Movie Rating Prediction with Matrix Factorization Algorithm
Attention for Chapter 24: Extending Social Network Analysis with Discourse Analysis: Combining Relational with Interpretive Data
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Chapter title
Extending Social Network Analysis with Discourse Analysis: Combining Relational with Interpretive Data
Chapter number 24
Book title
The Influence of Technology on Social Network Analysis and Mining
Published by
Springer Vienna, January 2013
DOI 10.1007/978-3-7091-1346-2_24
Book ISBNs
978-3-70-911345-5, 978-3-70-911346-2
Authors

Christine Moser, Peter Groenewegen, Marleen Huysman, Moser, Christine, Groenewegen, Peter, Huysman, Marleen

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 6%
Spain 1 3%
Unknown 28 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 48%
Student > Bachelor 3 10%
Lecturer > Senior Lecturer 2 6%
Researcher 2 6%
Lecturer 1 3%
Other 4 13%
Unknown 4 13%
Readers by discipline Count As %
Social Sciences 12 39%
Business, Management and Accounting 6 19%
Computer Science 2 6%
Agricultural and Biological Sciences 1 3%
Economics, Econometrics and Finance 1 3%
Other 3 10%
Unknown 6 19%