Towards an Information Theory of Complex Networks
Subtitle: Statistical Methods and Applications
By: edited by Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler
Contributors: Emmert-Streib, Frank; Mehler, Alexander, 1967-; SpringerLink (Online service)
Edition: 1.
Publisher: Springer Science+Business Media, LLC
Published: 2011
Place of publication: Boston
Language: eng
Format: [electronic resource] :
Number of pages: 395
ISBN-13: 9780817649036; 9780817649043
Subjects: Mathematics; Coding theory; Physiology; Artificial intelligence; Telecommunication
Description: 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.