Link Mining: Models, Algorithms, and Applications
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
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Philip S. Yu · Jiawei Han · Christos Faloutsos Editors
Link Mining: Models, Algorithms, and Applications
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Editors Philip S. Yu Department of Computer Science University of Illinois at Chicago 851 S. Morgan St. Chicago, IL 60607-7053, USA [email protected]
Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign 201 N. Goodwin Ave. Urbana, IL 61801, USA [email protected]
Christos Faloutsos School of Computer Science Carnegie Mellon University 5000 Forbes Ave. Pittsburgh, PA 15213, USA [email protected]
ISBN 978-1-4419-6514-1 e-ISBN 978-1-4419-6515-8 DOI 10.1007/978-1-4419-6515-8 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010932880 c Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
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 on various important applications such as Web and text mining, social network analysis, collaborative filtering, and bioinformatics. As an emerging research field, there are currently no books focusing on the theory and techniques as well as the related applications for link mining, especially from an interdisciplinary point of view. On the other hand, 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. Therefore, researchers and practitioners need a comprehensive book to systematically study, further develop, and apply the link mining techniques to these applications. This book contains contributed chapters from a variety of prominent researchers in the field. While the chapters are
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