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Mining and Analyzing Social Networks

  • Book
  • © 2010

Overview

Editors:
  1. I-Hsien Ting

    1. Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan 5

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  2. Hui-Ju Wu

    1. Department of Information Management , National University of Kaohsiung, Kaohsiung, Taiwan 5

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  3. Tien-Hwa Ho

    1. Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan 5

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  • Recent research on Mining and Analyzing Social Networks
  • Written by leading experts in this field
  • State-of-the-Art Book

Part of the book series: Studies in Computational Intelligence (SCI, volume 288)

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Table of contents (10 chapters)

  1. Front Matter

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  2. Graph Model for Pattern Recognition in Text

    • Qin Wu, Eddie Fuller, Cun-Quan Zhang
    Pages 1-20

  3. Retrieving Wiki Content Using an Ontology

    • Carlos Miguel Tobar, Alessandro Santos Germer, Juan Manuel Adán-Coello, Ricardo Luís de Freitas
    Pages 21-33

  4. Ego-Centric Network Sampling in Viral Marketing Applications

    • Huaiyu (Harry) Ma, Steven Gustafson, Abha Moitra, David Bracewell
    Pages 35-51

  5. Integrating SNA and DM Technology into HR Practice and Research: Layoff Prediction Model

    • Hui-Ju Wu, I-Hsien Ting, Huo-Tsan Chang
    Pages 53-66

  6. Actor Identification in Implicit Relational Data Sources

    • Michael Farrugia, Aaron Quigley
    Pages 67-89

  7. Perception of Online Social Networks

    • Travis Green, Aaron Quigley
    Pages 91-106

  8. Ranking Learning Entities on the Web by Integrating Network-Based Features

    • Yingzi Jin, Yutaka Matsuo, Mitsuru Ishizuka
    Pages 107-123

  9. Discovering Proximal Social Intelligence for Quality Decision Support

    • Yuan-Chu Hwang
    Pages 125-137

  10. Discovering User Interests by Document Classification

    • Loc Nguyen
    Pages 139-159

  11. Network Analysis of Opto-Electronics Industry Cluster: A Case of Taiwan

    • Ting-Lin LEE
    Pages 161-182

  12. Back Matter

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Keywords

About this book

Mining social networks has now becoming a very popular research area not only for data mining and web mining but also social network analysis. Data mining is a technique that has the ability to process and analyze large amount of data and by this to discover valuable information from the data. In recent year, due to the growth of social communications and social networking websites, data mining becomes a very important and powerful technique to process and analyze such large amount of data. Thus, this book will focus upon Mining and Analyzing social network. Some chapters in this book are extended from the papers that presented in MSNDS2009 (the First International Workshop on Mining Social Networks for Decision Support) and SNMABA2009 ((The International Workshop on Social Networks Mining and Analysis for Business Applications)). In addition, we also sent invitations to researchers that are famous in this research area to contribute for this book. The chapters of this book are introduced as follows: In chapter 1-Graph Model for Pattern Recognition in Text, Qin Wu et al. present a novel approach that uses a weighted directed multigraph for text pattern recognition. In the proposed methodology, a weighted directed multigraph model has been set up by using the distances between the keywords as the weights of arcs as well a keyword-frequency distance based algorithm has also been introduced. Case studies are also included in this chapter to show the performance is better than traditional means.

Editors and Affiliations

  • Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan 5

    I-Hsien Ting, Tien-Hwa Ho

  • Department of Information Management , National University of Kaohsiung, Kaohsiung, Taiwan 5

    Hui-Ju Wu

Bibliographic Information

  • Book Title: Mining and Analyzing Social Networks

  • Editors: I-Hsien Ting, Hui-Ju Wu, Tien-Hwa Ho

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-642-13422-7

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2010

  • Hardcover ISBN: 978-3-642-13421-0Published: 29 May 2010

  • Softcover ISBN: 978-3-642-26349-1Published: 28 June 2012

  • eBook ISBN: 978-3-642-13422-7Published: 16 May 2010

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: 200

  • Number of Illustrations: 39 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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