Link Mining: Models, Algorithms, and Applications

  • Philip S. Yu
  • Jiawei Han
  • Christos Faloutsos

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Link-Based Clustering

    1. Front Matter
      Pages 1-1
    2. Zhongfei (Mark) Zhang, Bo Long, Zhen Guo, Tianbing Xu, Philip S. Yu
      Pages 3-44
    3. Xiaoxin Yin, Jiawei Han, Philip S. Yu
      Pages 45-71
    4. Jimeng Sun, Spiros Papadimitriou, Philip S. Yu, Christos Faloutsos
      Pages 73-104
  3. Graph Mining and Community Analysis

    1. Front Matter
      Pages 105-105
    2. Galileo Mark Namata, Hossam Sharara, Lise Getoor
      Pages 107-133
    3. Pedro Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson et al.
      Pages 135-161
    4. Xin Li, Bing Liu, Philip S. Yu
      Pages 187-209
    5. Hanghang Tong, Spiros Papadimitriou, Philip S. Yu, Christos Faloutsos
      Pages 211-236
    6. Hong Cheng, Xifeng Yan, Jiawei Han
      Pages 237-262
  4. Link Analysis for Data Cleaning and Information Integration

    1. Front Matter
      Pages 263-263
    2. Ee-Peng Lim, Aixin Sun, Anwitaman Datta, Kuiyu Chang
      Pages 265-281
    3. Xiaoxin Yin, Jiawei Han, Philip S. Yu
      Pages 283-304
  5. Social Network Analysis

    1. Front Matter
      Pages 305-305
    2. Tanya Berger-Wolf, Chayant Tantipathananandh, David Kempe
      Pages 307-336
    3. Ravi Kumar, Jasmine Novak, Andrew Tomkins
      Pages 337-357
    4. Kenneth L. Clarkson, Kun Liu, Evimaria Terzi
      Pages 359-385
  6. Summarization and OLAP of Information Networks

    1. Front Matter
      Pages 387-387

About this book

Introduction

With the recent flourishing research activities on Web search and mining, social network analysis, information network analysis, information retrieval, link analysis, and structural data mining, research on link mining has been rapidly growing, forming a new field of data mining. Traditional data mining focuses on "flat" or “isolated” data in which each data object is represented as an independent attribute vector. However, many real-world data sets are inter-connected, much richer in structure, involving objects of heterogeneous types and complex links. Hence, the study of link mining will have a high impact in various important applications such as Web and text mining, social network analysis, collaborative filtering, and bioinformatics. Link Mining: Models, Algorithms and Applications focuses on the theory and techniques as well as the related applications for link mining, especially from an interdisciplinary point of view. Due to the high popularity of linkage data, extensive applications ranging from governmental organizations to commercial businesses to people's daily life call for exploring the techniques of mining linkage data. This book provides a comprehensive coverage of the link mining models, techniques and applications. Each chapter is contributed from some well known researchers in the field. Link Mining: Models, Algorithms and Applications is designed for researchers, teachers, and advanced-level students in computer science. This book is also suitable for practitioners in industry.

Keywords

Clustering algorithms bioinformatics classification collaborative filtering data cleansing data mining database databases machine learning text mining web mining

Editors and affiliations

  • Philip S. Yu
    • 1
  • Jiawei Han
    • 2
  • Christos Faloutsos
    • 3
  1. 1.Dept. Computer ScienceUniversity of Illinois, ChicagoChicagoUSA
  2. 2.Dept. Computer ScienceUniversity of Illinois, Urbana-ChampaignUrbanaUSA
  3. 3.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-6515-8
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, New York, NY
  • eBook Packages Biomedical and Life Sciences
  • Print ISBN 978-1-4419-6514-1
  • Online ISBN 978-1-4419-6515-8
  • About this book