Capítols de llibre
http://hdl.handle.net/2117/79833
2020-08-08T15:20:14ZBetweenness Centrality in Graphs
http://hdl.handle.net/2117/24904
Betweenness Centrality in Graphs
Gago Álvarez, Silvia; Coronicová Hurajová, Jana; Madaras, Tomas
The first book devoted exclusively to quantitative graph theory, Quantitative Graph Theory: Mathematical Foundations and Applications presents and demonstrates existing and novel methods for analyzing graphs quantitatively. Incorporating interdisciplinary knowledge from graph theory, information theory, measurement theory, and statistical techniques, this book covers a wide range of quantitative-graph theoretical concepts and methods, including those pertaining to real and random graphs such as:
Comparative approaches (graph similarity or distance)
Graph measures to characterize graphs quantitatively
Applications of graph measures in social network analysis and other disciplines
Metrical properties of graphs and measures
Mathematical properties of quantitative methods or measures in graph theory
Network complexity measures and other topological indices
Quantitative approaches to graphs using machine learning (e.g., clustering)
Graph measures and statistics
Information-theoretic methods to analyze graphs quantitatively (e.g., entropy)
Through its broad coverage, Quantitative Graph Theory: Mathematical Foundations and Applications fills a gap in the contemporary literature of discrete and applied mathematics, computer science, systems biology, and related disciplines. It is intended for researchers as well as graduate and advanced undergraduate students in the fields of mathematics, computer science, mathematical chemistry, cheminformatics, physics, bioinformatics, and systems biology.
2014-12-03T09:48:17ZGago Álvarez, SilviaCoronicová Hurajová, JanaMadaras, TomasThe first book devoted exclusively to quantitative graph theory, Quantitative Graph Theory: Mathematical Foundations and Applications presents and demonstrates existing and novel methods for analyzing graphs quantitatively. Incorporating interdisciplinary knowledge from graph theory, information theory, measurement theory, and statistical techniques, this book covers a wide range of quantitative-graph theoretical concepts and methods, including those pertaining to real and random graphs such as:
Comparative approaches (graph similarity or distance)
Graph measures to characterize graphs quantitatively
Applications of graph measures in social network analysis and other disciplines
Metrical properties of graphs and measures
Mathematical properties of quantitative methods or measures in graph theory
Network complexity measures and other topological indices
Quantitative approaches to graphs using machine learning (e.g., clustering)
Graph measures and statistics
Information-theoretic methods to analyze graphs quantitatively (e.g., entropy)
Through its broad coverage, Quantitative Graph Theory: Mathematical Foundations and Applications fills a gap in the contemporary literature of discrete and applied mathematics, computer science, systems biology, and related disciplines. It is intended for researchers as well as graduate and advanced undergraduate students in the fields of mathematics, computer science, mathematical chemistry, cheminformatics, physics, bioinformatics, and systems biology.