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OverviewIn the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, that even today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail. Full Product DetailsAuthor: Jörg ReichardtPublisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K Edition: Softcover reprint of hardcover 1st ed. 2009 Volume: 766 Dimensions: Width: 15.50cm , Height: 0.90cm , Length: 23.50cm Weight: 0.454kg ISBN: 9783642099656ISBN 10: 3642099653 Pages: 151 Publication Date: 22 October 2010 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contentsto Complex Networks.- Standard Approaches to Network Structure: Block Modeling.- A First Principles Approach to Block Structure Detection.- Diagonal Block Models as Cohesive Groups.- Modularity of Dense Random Graphs.- Modularity of Sparse Random Graphs.- Applications.- Conclusion and Outlook.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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