Adjacency Matrix
advanced discrete structures
Age Group_Uncategorized
Age Group_Uncategorized
algebraic graph theory
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B01=Ivan Gutman
B01=Matthias Dehmer
B01=Xueliang Li
B01=Yongtang Shi
Bipartite Graph
Bounce Path
Burning Test
Category1=Non-Fiction
Category=PBD
Category=PBV
Category=PBW
Category=UL
Characteristic Polynomial
Chromatic Polynomial
Clique Partition
combinatorial graph
combinatorial optimization
COP=United States
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discrete mathematics
Dyck Path
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Eulerian Circuits
Generalized Corona
Graph Homomorphisms
Graph Polynomials
graph theory
graph trees
Hexagonal Chain
Immanantal Polynomials
Independent Set
information sciences
Isotropic System
Language_English
mathematical graph invariants
network analysis methods
PA=Available
Parking Functions
Partition Function
Pendant Edges
polynomial invariants in mathematics
Polynomial Reconstructible
Price_€100 and above
PS=Active
Recurrent Configurations
Sandpile Model
softlaunch
Spectral Graph Theory
statistical mechanics models
Symmetric Trace
Tutte Polynomial
Product details
- ISBN 9781498755900
- Weight: 520g
- Dimensions: 178 x 254mm
- Publication Date: 06 Dec 2016
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
- Language: English
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This book covers both theoretical and practical results for graph polynomials. Graph polynomials have been developed for measuring combinatorial graph invariants and for characterizing graphs. Various problems in pure and applied graph theory or discrete mathematics can be treated and solved efficiently by using graph polynomials. Graph polynomials have been proven useful areas such as discrete mathematics, engineering, information sciences, mathematical chemistry and related disciplines.
Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his Ph.D. in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria), Vienna University of Technology, and University of Coimbra (Portugal). He obtained his habilitation in applied discrete mathematics from the Vienna University of Technology. Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria) and also has a position at Bundeswehr Universit¨at M¨unchen (Germany). His research interests are in graph theory, complex networks, complexity, machine learning and information theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology. He has more than 170 publications in applied mathematics, computer science and related disciplines.
Yongtang Shi studied mathematics at Northwest University (Xi’an, China) and received his Ph.D in applied mathematics from Nankai University (Tianjin, China). Currently, he is an associate professor at the Center for Combinatorics of Nankai University. He visited some institutes and universities at Germany, Austria and Canada. His research interests are in graph theory and its applications, especially the applications of graph theory in mathematical chemistry, computer science and information theory. He has about 50 publications in graph theory and its applications.
Ivan Gutman obtained his PhD degree in chemistry at the Faculty of Science, University of Zagreb, and also a PhD degree in mathematics, at the Faculty of Electrical Engineering, University of Belgrade. He is a member of the Serbian Academy of Sciences and Arts 1998; a member of the International Academy
