Overview
- Editors:
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Philip S. Yu
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Dept. Computer Science, University of Illinois, Chicago, Chicago, USA
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Jiawei Han
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Dept. Computer Science, University of Illinois, Urbana-Champaign, Urbana, USA
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Christos Faloutsos
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School of Computer Science, Carnegie Mellon University, Pittsburgh, USA
- Link mining has become an emerging field of data mining, which has a high impact in various important applications such as text mining, social network analysis, collaborative filtering, and bioinformatics
- This will be the first book on the market focusing on the theory and techniques as well as the related applications for link mining
- Presents in-depth surveys and systematic discussions on models, algorithms and applications for link mining
- Includes supplementary material: sn.pub/extras
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Table of contents (21 chapters)
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Summarization and OLAP of Information Networks
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- Yuanyuan Tian, Jignesh M. Patel
Pages 389-409
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- Chen Chen, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Raghu Ramakrishnan
Pages 411-438
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- Chen Chen, Cindy Xide Lin, Matt Fredrikson, Mihai Christodorescu, Xifeng Yan, Jiawei Han
Pages 475-501
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Analysis of Biological Information Networks
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Front Matter
Pages 503-503
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- Xiang Zhang, Feng Pan, Wei Wang
Pages 505-534
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- Young-Rae Cho, Aidong Zhang
Pages 535-556
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- Pinaki Sarder, Weixiong Zhang, J. Perren Cobb, Arye Nehorai
Pages 557-568
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Back Matter
Pages 569-586
About this book
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.
Editors and Affiliations
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Dept. Computer Science, University of Illinois, Chicago, Chicago, USA
Philip S. Yu
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Dept. Computer Science, University of Illinois, Urbana-Champaign, Urbana, USA
Jiawei Han
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School of Computer Science, Carnegie Mellon University, Pittsburgh, USA
Christos Faloutsos