Advances in Network Complexity

A well-balanced overview of mathematical approaches to complex systems ranging from applications in chemistry and ecology to basic research questions on network complexity. Matthias Dehmer, Abbe Mowshowitz, and Frank
Emmert-Streib, well-known pioneers in the fi eld, have edited this volume with a view to balancing classical and modern approaches to ensure broad coverage of contemporary research problems.
The book is a valuable addition to the literature and a must-have for anyone dealing with network compleaity and complexity issues.

Volume editors:

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 (Coimbra). He obtained his habilitation in applied discrete mathematics from the Vienna University of Technology. Currently, he is Professor at UMIT - TheHealth and Life Sciences University (Austria). His research interests are in bioinformatics, systems biology, network biology, complex networks, complexity 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.

Abbe Mowshowitz studied mathematics at the University of Chicago (BA 1961), and both mathematics and computer sciance at the University of Michigan (PhD 1967). He has held academic positions at the University of Toronto, The University of British Columbia, Erasmus University-Rotterdam, the University of Amsterdam and has been a professor of computer science at the City College of New York and in the PhD Program in Computer Science of the City University of New York since 1984. His research interests lie in applications of graph theory to the analysis of complex networks, and in the study of virtual organization.

Series Editors:

Matthias Dehmer (See above)

Frank Emmert-Streib studied physics at the University of Siegen (Germany) and received his Ph.D. in Theoretical Physics from the University of Bremen (Germany). He was a postdoctoral research associate at the Stowers Institute for Medical Research (Kansas City, USA) in the Department for Bioinformatics and a Senior Fellow at the University of Washington (Seattle, USA) in the Department of Biostatistics and the Department of Genome Sciences. Currently, he is Lecturer/Assistant Professor at the Queen?s University Belfast at the Center for Cancer Research and Cell Biology (CCRCB) leading the Computational Biology and Machine Learning Lab. His research interests are in the ?eld of Computational Biology, Machine Learning and Biostatistics in the development and application of methods from statistics and machine learning for the analysis of high-throughput data from genomics and genetics experiments.

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) and at the Vienna University of Technology. Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria) leading the Insititute for Bioinformatics and Translational Research. His research interests are in bioinformatics, systems biology, complex networks and statistics. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational and systems biology.

Preface

1. Information-theoretic approaches to graph and set complexity, structure dynamics relationships in networks, and applications to biological systems.
Ilya Shmulevich et al., USA

2. Computational Complexity and Graphs
Angel Garrido, Spain

3. Characterizing Metro Networks: State, Form, and Structure
Sybil Derrible, Canada

4. Shannon Entropy and Degree Correlations in Complex Networks
Samuel Johnson, UK

5. Concepts of Network Complexity for biomedical Applications.
Enrico Capobianco, Italy

6. Functional Complexity Based on Topology
Hildegard Meyer-Ortmanns, Germany

7. Computational complexity of graphs
Stasys Jukna, Germany

8. Emergence of heterogeneous structures in chemical reaction-diffusion networks
Qi Xuan, China

9. Gibbs entropy of network ensembles
Simone Severini and Ginestra Bianconi, USA

10. Information based complexity of networks
Russell Standish, Australia

11. The Linear Complexity of a Graph
David Neel et al., USA

12. Complexity management for visualizing gene networks
N.N

13. Unraveling gene regulatory networks from time-resolved gene expression
Data
N.N

14. Biological Network Reconstruction and Complexity
N.N

15. Predicting community responses to perturbations in the face of imperfect knowledge and network complexity
N.N

16. Neural complexity: a graph theoretic interpretation
N.N

17. Path lengths in protein-protein interaction networks and biological complexity
N.N