Towards an information theory of complex networks : statistical methods and applications
- New York : Birkhäuser, 2011.
- Physical description
- xvi, 395 p. : ill. ; 24 cm.
QA402 .T69 2011
- Unknown QA402 .T69 2011
- Includes bibliographical references.
- 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.
- (source: Nielsen Book Data)
- Publisher's Summary
- 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. 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.
(source: Nielsen Book Data)
- System analysis.
- Publication date
- Matthias Dehmer, Frank Emmert-Streib, Alexander Mehler, editors.