Analysis of Complex Networks
From Biology to Linguistics
Quantitative and Network Biology
1. Edition April 2009
XVIII, 462 Pages, Hardcover
91 Pictures (8 Colored Figures)
15 tables
Practical Approach Book
Short Description
This title first gives an overview of graph theory followed by applications of this field in biomedical (e.g. systems biology) and computational science. Based on a novel, comprehensive conceptwith contributions by renowned experts in the field.
Further versions
Mathematical problems such as graph theory problems are of increasing importance for the analysis of modelling data in biomedical research such as in systems biology, neuronal network modelling etc. This book follows a new approach of including graph theory from a mathematical perspective with specific applications of graph theory in biomedical and computational sciences. The book is written by renowned experts in the field and offers valuable background information for a wide audience.
Statistical Mechanics of Complex Networks (Thurner)
A Simple Integrated Approach to Network Complexity and Node Centrality (Bonchev)
Graph Spectra and the Structure of Complex Networks (Estrada)
Random Induced Subgraphs of n-Cubes (Reidys)
Graph Edit Distance - Optimal and Suboptimal Algorithms with Applications (Bunke, Riesen)
Graph Energy (Gutman, Li, Zhang)
Generalized Shortest Path Trees: A Novel Graph Class by Example of Semiotic Networks (Mehler)
Applications of Graph Theory in Chemo- and Bioinformatics (Dimitropoulos, Golovin, John, Krissinel) Structural and Functional Dynamics in Cortical and Neuronal Networks (Kaiser, Simonotto)
Network Mapping of Metabolic Pathways (Cheng, Zelikovsky)
Graph Structure Analysis and Computational Tractability of Scheduling Problems (Sevastyanov, Kononov)
Counting Cubes in Median Graphs and Related Problems (Kovse)
Elementary Elliptic (R, q)-Polycycles (Deza, Sikiric, Shtogrin)
Optimal Dynamic Flows in Networks and Algorithms for Finding Them (Lozovanu, Fonoberova)
Analyzing and Modeling European R&D Collaborations: Challenges and Opportunities from a Large Social Network (Barber, Paier, Scherngell)
Analytic Combinatorics on Random Graphs (Drmota, Gittenberger)
Frank Emmert-Streib studied Physics at the University of Siegen (Germany) and received his Ph.D. in Theoretical Physics form the University of Bremen (Germany). He was postdoctoral research associate at the Stowers Institute for Medical Research (Kansas City, USA) in the Department for Bioinformatics and is currently senior research fellow at the University of Washington (Seattle, USA) in Biostatistics and Genome Sciences. His research interests are in the field of Computational Biology, Biostatistics, Machine Learning and Information Theory and Statistical Learning.
