Towards an information theory of complex networks : statistical methods and applications
 Responsibility
 Matthias Dehmer, Frank EmmertStreib, Alexander Mehler, editors.
 Language
 English.
 Imprint
 New York : Birkhäuser, 2011.
 Physical description
 xvi, 395 p. : ill. ; 24 cm.
Access
Creators/Contributors
Contents/Summary
 Bibliography
 Includes bibliographical references.
 Contents

 Preface. Entropy of Digraphs and Infinite Networks. An InformationTheoretic Upper Bound on Planar Graphs Using Wellorderly Maps. Probabilistic Inference Using Function Factorization and Divergence Minimization. Wave Localization on Complex Networks. InformationTheoretic Methods in Chemical Graph Theory. On the Development and Application of NetSign Graph Theory. The Central Role of Information Theory in Ecology. Inferences About Coupling from Ecological Surveillance Monitoring. Markov Entropy Centrality. Social Ontologies as Generalizedd Nearly Acyclic Directed Graphs. Typology by Means of Language Networks. Information TheoryBased Measurement of Software. Fair and Biased Random Walks on Undirected Graphs and Related Entropies.
 (source: Nielsen Book Data)
 Publisher's Summary
 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 carefullyselected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about informationtheoretic 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 graphtheoretic, informationtheoretic, and statistical methods as a way to better understand and characterize realworld networks. This volume is the first to present a selfcontained, comprehensive overview of informationtheoretic models of complex networks with an emphasis on applications. As such, it marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines and can serve as a valuable resource for a diverse audience of advanced students and professional scientists. While it is primarily intended as a reference for research, the book could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.
(source: Nielsen Book Data)
Subjects
 Subject
 System analysis.
Bibliographic information
 Publication date
 2011
 ISBN
 9780817649036 (hbk.)
 0817649034 (hbk.)
 9780817649043 (ebk.)
 0817649042 (ebk.)