Quantitative Graph Theory

Mathematical Foundations and Applications

Matthias Dehmer, Frank Emmert-Streib

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English

CRC Press Inc
27 October 2014

Combinatorics & graph theory

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.

Edited by: Matthias Dehmer, Frank Emmert-Streib
Imprint: CRC Press Inc
Country of Publication: United States
Volume: 85
Dimensions: Height: 234mm, Width: 156mm, Spine: 28mm
Weight: 907g
ISBN: 9781466584518
ISBN 10: 1466584513
Pages: 528
Publication Date: 27 October 2014
Audience: Professional and scholarly , Professional and scholarly , Undergraduate , Undergraduate
Format: Hardback
Publisher's Status: Active

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.

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.

Reviews for Quantitative Graph Theory: Mathematical Foundations and Applications

... includes some papers with particular emphasis in the applications of quantitative graph theory. ... It is always very nice when we learn of such varied applications of mathematics in general and graph theory in particular. Most of the chapters should be accessible to graduate students. Some of them also include quite a large bibliography for further reference. ... this book fills indeed a gap in the discrete mathematics literature and is going to improve the status of quantitative graph theory. -Zentralblatt MATH 1310 The editors have done a nice job collecting articles that will be accessible to most graduate students in mathematics. ... these articles will give an interesting taste of some exciting mathematics and give the reader plenty of ideas of where to go to learn more. If nothing else, this collection will convince readers that graph theory, or at least large parts of it, belongs solidly under the category of applied mathematics, and that there is very interesting work being done in the area. -Darren Glass, MAA Reviews, January 2015