Towards an Information Theory of Complex Networks (PDF)
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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.
- 2011, 2011, 395 Seiten, Englisch
- Herausgegeben: Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler
- Verlag: Springer Basel AG
- ISBN-10: 0817649042
- ISBN-13: 9780817649043
- Erscheinungsdatum: 26.08.2011
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