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Towards an Information Theory of Complex Networks Statistical Methods and Applications /
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 Net...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boston, MA :
Birkhäuser Boston : Imprint: Birkhäuser,
2011.
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| Edición: | 1st ed. 2011. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-0-8176-4904-3 | ||
| 003 | DE-He213 | ||
| 005 | 20220117022525.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 110824s2011 xxu| s |||| 0|eng d | ||
| 020 | |a 9780817649043 |9 978-0-8176-4904-3 | ||
| 024 | 7 | |a 10.1007/978-0-8176-4904-3 |2 doi | |
| 050 | 4 | |a QA76.9.M35 | |
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| 072 | 7 | |a UYA |2 thema | |
| 082 | 0 | 4 | |a 004.0151 |2 23 |
| 245 | 1 | 0 | |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. |
| 250 | |a 1st ed. 2011. | ||
| 264 | 1 | |a Boston, MA : |b Birkhäuser Boston : |b Imprint: Birkhäuser, |c 2011. | |
| 300 | |a XVI, 395 p. 114 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 505 | 0 | |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. | |
| 520 | |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. | ||
| 650 | 0 | |a Computer science-Mathematics. | |
| 650 | 0 | |a Coding theory. | |
| 650 | 0 | |a Information theory. | |
| 650 | 0 | |a Biomathematics. | |
| 650 | 0 | |a Telecommunication. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Mathematics. | |
| 650 | 1 | 4 | |a Mathematical Applications in Computer Science. |
| 650 | 2 | 4 | |a Coding and Information Theory. |
| 650 | 2 | 4 | |a Mathematical and Computational Biology. |
| 650 | 2 | 4 | |a Communications Engineering, Networks. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Applications of Mathematics. |
| 700 | 1 | |a Dehmer, Matthias. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Emmert-Streib, Frank. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Mehler, Alexander. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9780817649050 |
| 776 | 0 | 8 | |i Printed edition: |z 9780817649036 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-0-8176-4904-3 |z Texto Completo |
| 912 | |a ZDB-2-SMA | ||
| 912 | |a ZDB-2-SXMS | ||
| 950 | |a Mathematics and Statistics (SpringerNature-11649) | ||
| 950 | |a Mathematics and Statistics (R0) (SpringerNature-43713) | ||