Quantitative Graph Theory

Mathematical Foundations and Applications
Herausgeber: Dehmer, Matthias; Emmert-Streib, Frank

• Broschiertes Buch

Produktbeschreibung

This is the first book devoted exclusively to quantitative graph theory. It 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. Through its broad coverage, this text fills a gap in the contemporary literature of discrete and applied mathematics, computer science, systems biology, and related disciplines.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 528
• Erscheinungstermin: 2. Oktober 2019
• Englisch
• Abmessung: 231mm x 155mm x 30mm
• Gewicht: 726g
• ISBN-13: 9780367378226
• ISBN-10: 0367378221
• Artikelnr.: 58052446

Autorenporträt

Matthias Dehmer studied mathematics and computer science at the University of Siegen, Germany, and earned his Ph.D in computer science from the Darmstadt University of Technology. He held research positions at the University of Rostock (Germany), Vienna Bio Center (Austria), Vienna Technical University (Austria), and University of Coimbra (Portugal), and obtained his habilitation in applied discrete mathematics from the Vienna University of Technology. His research focuses on investigating network-based methods in the context of systems biology, structural graph theory, operations research, and information theory. He has over 180 peer-reviewed publications, is an editor of a book series and a member of multiple editorial boards, and has co/organized several scientific conferences. Frank Emmert-Streib studied physics at the University of Siegen, Germany, and earned his Ph.D in theoretical physics from the University of Bremen. After postdoc positions in the United States, he joined the Center for Cancer Research and Cell Biology at the Queen's University Belfast (United Kingdom), where he is currently an associate professor (senior lecturer) leading the Computational Biology and Machine Learning Laboratory. His research interests are in the fields of computational biology, biostatistics, and network medicine and are focused on the development and application of methods from statistics and machine learning for the analysis of high-dimensional data from genomics experiments.

Inhaltsangabe

What Is Quantitative Graph Theory?. Localization of Graph Topological
Indices via Majorization Technique. Wiener Index of Hexagonal Chains with
Segments of Equal Length. Metric-Extremal Graphs. Quantitative Methods for
Nowhere-Zero Flows and Edge Colorings. Width-Measures for Directed Graphs
and Algorithmic Applications. Betweenness Centrality in Graphs. On a
Variant Szeged and PI Indices of Thorn Graphs. Wiener Index of Line Graphs.
Single-Graph Support Measures. Network Sampling Algorithms and
Applications. Discrimination of Image Textures Using Graph Indices. Network
Analysis Applied to the Political Networks of Mexico. Social Network
Centrality, Movement Identification, and the Participation of Individuals
in a Social Movement: The Case of the Canadian Environmental Movement.
Graph Kernels in Chemoinformatics. Chemical Compound Complexity in
Biological Pathways.