Towards an Information Theory of Complex Networks : Statistical Methods and Applications

Dehmer, M. (EDT)/ Emmert-Streib, F. (EDT)

Springer（2011/08発売）

• 製本 Hardcover:ハードカバー版／ページ数 340 p.／サイズ 100 illus.
• 言語 ENG
• 商品コード 9780817649036

基本説明

Synthesizes graph-theoretic, statistical, and informationtheoretic methods to effectively understand and characterize real-world networks.

Full 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. 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.