Towards an Information Theory of Complex Networks
Statistical Methods and Applications
Editors: Dehmer, Matthias, Emmert-Streib, Frank, Mehler, Alexander (Eds.)
Free Preview- First book on the market giving a comprehensive look at the applications of information-theoretic models for complex networks
- Synthesizes graph-theoretic, statistical, and information-theoretic methods to effectively understand and characterize real-world networks
- Addresses a broad range of disciplines, including quantitative biology, quantitative chemistry, quantitative sociology, and quantitative linguistics
- Caters to both researchers and scholars across the sciences
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- About this book
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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:
- chemical graph theory
- ecosystem interaction dynamics
- social ontologies
- language networks
- software systems
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.
- Table of contents (13 chapters)
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Entropy of Digraphs and Infinite Networks
Pages 1-16
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An Information-Theoretic Upper Bound on Planar Graphs Using Well-Orderly Maps
Pages 17-46
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Probabilistic Inference Using Function Factorization and Divergence Minimization
Pages 47-74
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Wave Localization on Complex Networks
Pages 75-96
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Information-Theoretic Methods in Chemical Graph Theory
Pages 97-126
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Table of contents (13 chapters)
- Download Preface 1 PDF (63.5 KB)
- Download Sample pages 1 PDF (660.9 KB)
- Download Table of contents PDF (43.7 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Towards an Information Theory of Complex Networks
- Book Subtitle
- Statistical Methods and Applications
- Editors
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- Matthias Dehmer
- Frank Emmert-Streib
- Alexander Mehler
- Copyright
- 2011
- Publisher
- Birkhäuser Basel
- Copyright Holder
- Springer Science+Business Media, LLC
- eBook ISBN
- 978-0-8176-4904-3
- DOI
- 10.1007/978-0-8176-4904-3
- Hardcover ISBN
- 978-0-8176-4903-6
- Edition Number
- 1
- Number of Pages
- XVI, 395
- Number of Illustrations
- 114 b/w illustrations
- Topics
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