Analysis of Complex Networks

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.Neue Ansätze zur Analyse komplexer Netzwerke sind Gegenstand dieses Bandes, der mit einer Fülle mathematischer Hintergrundinformationen aufwartet. Mehr interessanten Beiträgen für Fachleute aus der Systembiologie oder der Analyse neuronaler Netze. Entropy, Orbits, and Spectra of Graphs (Mowshowitz, Mitsou)
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) 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). Currently, he is with the Vienna University of Technology (Austria) in the department of Discrete Mathematics and Geometry. His research interests are Graph Theory, Information Theory, Computational Biology and Machine Learning.

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.