Link Mining: Models, Algorithms, and Applications

1. Front Matter

   Pages i-xiii

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2. Link-Based Clustering

   1. Front Matter

      Pages 1-1

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   2. Machine Learning Approaches to Link-Based Clustering

      • Zhongfei (Mark) Zhang, Bo Long, Zhen Guo, Tianbing Xu, Philip S. Yu

      Pages 3-44

   3. Scalable Link-Based Similarity Computation and Clustering

      • Xiaoxin Yin, Jiawei Han, Philip S. Yu

      Pages 45-71

   4. Community Evolution and Change Point Detection in Time-Evolving Graphs

      • Jimeng Sun, Spiros Papadimitriou, Philip S. Yu, Christos Faloutsos

      Pages 73-104

3. Graph Mining and Community Analysis

   1. Front Matter

      Pages 105-105

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   2. A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks

      • Galileo Mark Namata, Hossam Sharara, Lise Getoor

      Pages 107-133

   3. Markov Logic: A Language and Algorithms for Link Mining

      • Pedro Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson et al.

      Pages 135-161

   4. Understanding Group Structures and Properties in Social Media

      • Lei Tang, Huan Liu

      Pages 163-185

   5. Time Sensitive Ranking with Application to Publication Search

      • Xin Li, Bing Liu, Philip S. Yu

      Pages 187-209

   6. Proximity Tracking on Dynamic Bipartite Graphs: Problem Definitions and Fast Solutions

      • Hanghang Tong, Spiros Papadimitriou, Philip S. Yu, Christos Faloutsos

      Pages 211-236

   7. Discriminative Frequent Pattern-Based Graph Classification

      • Hong Cheng, Xifeng Yan, Jiawei Han

      Pages 237-262

4. Link Analysis for Data Cleaning and Information Integration

   1. Front Matter

      Pages 263-263

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   2. Information Integration for Graph Databases

      • Ee-Peng Lim, Aixin Sun, Anwitaman Datta, Kuiyu Chang

      Pages 265-281

   3. Veracity Analysis and Object Distinction

      • Xiaoxin Yin, Jiawei Han, Philip S. Yu

      Pages 283-304

5. Social Network Analysis

   1. Front Matter

      Pages 305-305

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   2. Dynamic Community Identification

      • Tanya Berger-Wolf, Chayant Tantipathananandh, David Kempe

      Pages 307-336

   3. Structure and Evolution of Online Social Networks

      • Ravi Kumar, Jasmine Novak, Andrew Tomkins

      Pages 337-357

   4. Toward Identity Anonymization in Social Networks

      • Kenneth L. Clarkson, Kun Liu, Evimaria Terzi

      Pages 359-385

6. Summarization and OLAP of Information Networks

   1. Front Matter

      Pages 387-387

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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.

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

• Dept. Computer Science, University of Illinois, Chicago, Chicago, USA

  Philip S. Yu

• Dept. Computer Science, University of Illinois, Urbana-Champaign, Urbana, USA

  Jiawei Han

• School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

  Christos Faloutsos

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