For over a decade, complex networks have steadily grown as an important tool across a broad array of academic disciplines, with applications ranging from physics to social media. A tightly organized collection of carefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. The book's major goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks.
This volume is the first to present a self-contained, comprehensive overview of information-theoretic models of complex networks with an emphasis on applications. It begins with four chapters developing the most significant formal-theoretical issues of network modeling, but the majority of the book is devoted to combining theoretical results with an empirical analysis of real networks. Specific topics include:
This work marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines. As such, it can serve as a valuable resource for a diverse audience of advanced students and professional scientists. It is primarily intended as a reference for research, but could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.
Matthias Dehmer Frank Emmert-Streib Alexander Mehler Editors Towards an Information Theory of Complex Networks Statistical Methods and Applications Editors Matthias Dehmer UMIT Institute of Bioinformatics and Translational Research Eduard-Walln¨ofer-Zentrum I A-6060 Hall in Tirol Austria
[email protected] Frank Emmert-Streib School of Medicine, Dentistry and Biomedical Sciences Center for Cancer Research and Cell Biology Queen’s University Belfast 97 Lisburn Road Belfast BT9 7BL United Kingdom
[email protected] Alexander Mehler Faculty of Computer Science and Mathematics Goethe-University Frankfurt am Main Robert-Mayer-Straße 10 P.O. Box: 154 D-60325 Frankfurt am Main Germany
[email protected] ISBN 978-0-8176-4903-6 e-ISBN 978-0-8176-4904-3 DOI 10.1007/978-0-8176-4904-3 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011932673 Mathematics Subject Classification (2010): 68R10, 68P30, 94C15 c Springer ScienceCBusiness Media, LLC 2011 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 expre