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＜電子ブック＞
Towards an Information Theory of Complex Networks : Statistical Methods and Applications / edited by Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler

版	1st ed. 2011.
出版者	(Boston, MA : Birkhäuser Boston : Imprint: Birkhäuser)
出版年	2011
本文言語	英語
大きさ	XVI, 395 p. 114 illus : online resource
著者標目	Dehmer, Matthias editor Emmert-Streib, Frank editor Mehler, Alexander editor SpringerLink (Online service)
件　名	LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Coding theory LCSH:Information theory LCSH:Biomathematics LCSH:Telecommunication LCSH:Artificial intelligence LCSH:Mathematics FREE:Mathematical Applications in Computer Science FREE:Coding and Information Theory FREE:Mathematical and Computational Biology FREE:Communications Engineering, Networks FREE:Artificial Intelligence FREE:Applications of Mathematics
一般注記	Preface -- Entropy of Digraphs and Infinite Networks -- An Information-Theoretic Upper Bound on Planar Graphs Using Well-orderly Maps -- Probabilistic Inference Using Function Factorization and Divergence Minimization -- Wave Localization on Complex Networks -- Information-Theoretic Methods in Chemical Graph Theory -- On the Development and Application of Net-Sign Graph Theory -- The Central Role of Information Theory in Ecology -- Inferences About Coupling from Ecological Surveillance Monitoring -- Markov Entropy Centrality -- Social Ontologies as Generalizedd Nearly Acyclic Directed Graphs -- Typology by Means of Language Networks -- Information Theory-Based Measurement of Software -- Fair and Biased Random Walks on Undirected Graphs and Related Entropies 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 HTTP:URL=https://doi.org/10.1007/978-0-8176-4904-3

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