Towards an Information Theory of Complex Networks Statistical Method
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-select
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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 expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper www.birkhauser-science.com
Preface
For more than a decade, complex network analysis has evolved as a methodological paradigm for a multitude of disciplines, including physics, chemistry, biology, geography, sociology, computer science, statistics, media science, and linguistics. Researchers in these fields share an interest in information processing subject to the networking of their corresponding research object, for instance, genes, molecules, individuals, semes, memes, etc. They start with the insight that any of these research objects is extrinsically characterized, if not constituted, by its networking with objects of the same provenance. In this way, networks, for example, gene networks, food networks, city networks, networks of words, sentences, texts, or web documents become important research objects in more and more disciplines. This book, in line with these research developments, presents theoretical and practical results of statistical models of complex networks in the formal sciences, the natural sciences, and the humanities. One of its goals is to advocate and promote combinations of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks. On the one hand, networks appear as paradigmatic objects of approaches throughout the natural and social sciences and th
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