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Towards an Information Theory of Complex Networks : Statistical Methods and Applications / edited by Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler
版 | 1st ed. 2011. |
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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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電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
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電子ブック | オンライン | 電子ブック |
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Springer eBooks | 9780817649043 |
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EB00231228 |
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データ種別 | 電子ブック |
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分 類 | LCC:QA76.9.M35 DC23:004.0151 |
書誌ID | 4000116054 |
ISBN | 9780817649043 |
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