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Table of contents (8 chapters)
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Front Matter
Keywords
About this book
In 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.
Authors and Affiliations
Bibliographic Information
Book Title: Structure in Complex Networks
Authors: J. Reichardt
Series Title: Lecture Notes in Physics
DOI: https://doi.org/10.1007/978-3-540-87833-9
Publisher: Springer Berlin, Heidelberg
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2009
Hardcover ISBN: 978-3-540-87832-2Published: 24 November 2008
Softcover ISBN: 978-3-642-09965-6Published: 22 October 2010
eBook ISBN: 978-3-540-87833-9Published: 04 November 2008
Series ISSN: 0075-8450
Series E-ISSN: 1616-6361
Edition Number: 1
Number of Pages: XIII, 151
Topics: Theory of Computation, Algorithm Analysis and Problem Complexity, Artificial Intelligence, Complex Systems, Economic Theory/Quantitative Economics/Mathematical Methods, Statistical Physics and Dynamical Systems