9780817649036 - towards an information theory of complex networks: statistical methods and applications (6 Ergebnisse)
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hardcover. Zustand: Sehr gut. 411 Seiten; 9780817649036.2 Gewicht in Gramm: 1.
Towards an Information Theory of Complex Networks: Statistical Methods and Applications
Dehmer, Matthias (Editor)/ Emmert-streib, Frank (Editor)/ Mehler, Alexander (Editor)
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Towards an Information Theory of Complex Networks: Statistical Methods and Applications
. Ed(s): Dehmer, Matthias; Emmert-Streib, Frank; Mehler, Alexander
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Zustand: New. Pesents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. This title advocates and promotes a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize r…eal-world networks. Editor(s): Dehmer, Matthias; Emmert-Streib, Frank; Mehler, Alexander. Num Pages: 395 pages, 114 black & white illustrations, 39 black & white tables, biography. BIC Classification: GPFC; PBT; PBW; TJK. Category: (P) Professional & Vocational. Dimension: 244 x 163 x 28. Weight in Grams: 740. . 2011. 2011th Edition. hardcover. . . . . Books ship from the US and Ireland.
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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 412 | Sprache: Englisch | Produktart: Bücher | 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 pap…ers 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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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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, To…wards 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.
