| Dewey Class |
519 |
| Title |
Towards an Information Theory of Complex Networks (EB) : Statistical Methods and Applications / edited by Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler. |
| Author |
Dehmer, Matthias |
| Added Personal Name |
Emmert-Streib, Frank |
| Mehler, Alexander |
| Other name(s) |
SpringerLink (Online service) |
| Edition statement |
1.st ed. |
| Publication |
Boston : Birkhäuser , 2011. |
| Physical Details |
XVI, 395 pages : 114 illus. : online resource. |
| ISBN |
9780817649043 |
| Summary Note |
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. tightly organized collection's 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.: |
| Contents note |
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. |
| System details note |
Online access to this digital book is restricted to susbcription institutions through IP address (only for SISSA internal users) |
| Internet Site |
http://dx.doi.org/10.1007/978-0-8176-4904-3 |
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