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|a 9780817649043
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|a 10.1007/978-0-8176-4904-3
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|a Towards an Information Theory of Complex Networks
|h [electronic resource] :
|b Statistical Methods and Applications /
|c edited by Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler.
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| 250 |
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|a 1st ed. 2011.
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| 264 |
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1 |
|a Boston, MA :
|b Birkhäuser Boston :
|b Imprint: Birkhäuser,
|c 2011.
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| 300 |
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|a XVI, 395 p. 114 illus.
|b online resource.
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|a 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.
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| 520 |
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|a 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.
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| 590 |
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|a Loaded electronically.
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| 590 |
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|a Electronic access restricted to members of the Holy Cross Community.
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| 650 |
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|a Information theory.
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| 650 |
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|a Coding theory.
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| 650 |
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|a Biomathematics.
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| 650 |
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|a Electrical engineering.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Applied mathematics.
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| 650 |
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|a Engineering mathematics.
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| 690 |
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|a Electronic resources (E-books)
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| 700 |
1 |
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|a Dehmer, Matthias.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Emmert-Streib, Frank.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Mehler, Alexander.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 710 |
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|a SpringerLink (Online service)
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