Dehmer, Matthias, Emmert-Streib, Frank, Mehler, Alexander (Eds.)
2011, XVI, 395p. 114 illus..
A product of Birkhäuser Basel
Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.
You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.
After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.
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
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
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.
Content Level »Research
Keywords »complexity - data analysis - entropy - information theory - networks