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

Editat de Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler
en Limba Engleză Hardback – 30 aug 2011
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.
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Specificații

ISBN-13: 9780817649036
ISBN-10: 0817649034
Pagini: 395
Ilustrații: XVI, 395 p. 114 illus.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.75 kg
Ediția:2011
Editura: Birkhäuser Boston
Colecția Birkhäuser
Locul publicării:Boston, MA, United States

Public țintă

Research

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

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 Includes supplementary material: sn.pub/extras