Advances in Network Complexity (Quantitative and Network Biology Series)

Matthias Dehmer; Abbe Mowshowitz; Frank Emmert-Streib

ISBN 10: 352733291X / ISBN 13: 9783527332915

Edité par Wiley-Blackwell, 2013

A propos de cet article

Hardcover, xiv+293pp, NOT ex-library. Clean and bright throughout, unmarked text, no inscriptions, no stamps, firmly bound. A minor crease to the lower outer corner of last pages. Short creases to board corners. Issued without a dust jacket. -- A well-balanced overview of mathematical approaches to complex systems ranging from applications in chemistry and ecology to basic research questions on network complexity. Well-known pioneers in the field have edited this volume with a view to balancing classical and modern approaches to ensure broad coverage of contemporary research problems. It is a valuable addition to the literature and a must-have for anyone dealing with network complexity and complexity issues. -- Contents: 1 Functional Complexity Based on Topology [Measure for the Functional Complexity of Networks; Applications] 2 Connections Between Artificial Intelligence & Computational Complexity & Complexity of Graphs [Representation & Searching Methods; Turing Machines; Fuzzy Logic & Graphs; Fuzzy Optimization & Systems; Problems Related to AI; Topology of Complex Networks; Hierarchies; Graph Entropy; Kolmogorov Complexity] 3 Selection-Based Estimates of Complexity Unravel Some Mechanisms & Selective Pressures Underlying the Evolution of Complexity in Artificial Networks [Complexity & Evolution; Macroscopic Quantification of Organismal Complexity; Selection-Based Methods of Complexity; Informational Complexity; Fisher Geometric Model; Cost of Complexity; Quantifying Phenotypic Complexity; Darwinian Adaptive Neural Networks DANN; Different Facets of Complexity; Mechanistic Understanding of Phenotypic Complexity; Selective Pressures Acting on Phenotypic Complexity] 4 Three Types of Network Complexity Pyramid [First Type: Life's Complexity Pyramid LCP; Second Type: Network Model Complexity Pyramid; Third Type: Generalized Farey Organized Network Pyramid] 5 Computational Complexity of Graphs [Star Complexity of Graphs; From Graphs to Boolean Functions; Formula Complexity of Graphs; Lower Bounds via Graph Entropy; Depth-2 & Depth-3 Complexity; Network Complexity of Graphs] 6 Linear Complexity of a Graph [Rationale & Approach; Background; Exploration of Irreducible Graphs; Bounds on the Linear Complexity of Graphs; Some Families of Graphs; Bounds for Graphs in General] 7 Kirchhoff's Matrix-Tree Theorem Revisited: Counting Spanning Trees with the Quantum Relative Entropy [Main Result; Bounds] 8 Dimension Measure for Complex Networks [Volume Dimension; Complex Network Zeta Function & Relation to Kolmogorov Complexity; Comparison with Complexity Classes; Node-Based Definition; Linguistic-Analysis Application; Statistical Mechanics Application; Function Values; Other Work on Complexity Measures] 9 Information-Based Complexity of Networks [History & Concept of Information-Based Complexity; Mutual Information; Graph Theory & Graph Theoretic Measures: Cyclomatic Number, Spanning Trees; Erdos-Renyi Random Graphs, Small World Networks, Scale-free Networks; Graph Entropy; Information-Based Complexity of Unweighted, Unlabeled & Undirected Networks; Motif Expansion; Labeled Networks; Weighted Networks; Empirical Results of Real Network Data & Artificially Generated Networks; Extension to Processes on Networks; Transfer Entropy; Medium Articulation] 10 Thermodynamic Depth in Undirected & Directed Networks [Polytopal vs Heat Flow Complexity; Characterization of Polytopal & Flow Complexity; Laplacian of a Directed Graph; Directed Heat Kernels & Heat Flow; Heat Flow-Thermodynamic Depth Complexity; Experimental Results] 11 Circumscribed Complexity in Ecological Networks [New Metaphor; Entropy as a Descriptor of Structure; Addressing Both Topology & Magnitude; Amalgamating Topology with Magnitudes; Effective Network Attributes; Limits to Complexity; Example Ecosystem Network; New Window on Complex Dynamics] 12 Metros as Biological Systems: Complexity in Small Real-life Networks [Methodology; Interpreting Complexity; Network Centrality]; Index.

N° de réf. du vendeur 007701

Détails bibliographiques

• Titre: Advances in Network Complexity (Quantitative and Network Biology Series)
• Éditeur: Wiley-Blackwell
• Date de parution: 2013
• Langue: anglais
• ISBN à 10 chiffres: 352733291X
• ISBN à 13 chiffres: 9783527332915
• Reliure: Hardcover
• Édition: 1st Edition
• État de l'article: Very Good

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Matthias Dehmer studied mathematics at the University of Siegen, Germany, and received his PhD in computer science from the Darmstadt University of Technology, Germany. Afterwards, he was a research fellow at Vienna Bio Center, Austria, Vienna University of Technology and University of Coimbra, Portugal. Currently, he is Professor at UMIT - The Health and Life Sciences University, Austria, and is Head of the Institute for Bioinformatics and TranslationalResearch. His research interests are in bioinformatics, chemical graph theory, systems biology, complex networks, complexity, statistics and information theory. He has published extensively on network complexity and methods to analyze complex networks quantitatively.

Abbe Mowshowitz studied mathematics at the University of Chicago (BA 1961), and both mathematics and computer science 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.

Frank Emmert-Streib studied physics at the University of Siegen, Germany, gaining his PhD in theoretical physics from the University of Bremen. He was a postdoctoral research associate at the Stowers Institute for Medical Research, Kansas City, USA, and a senior fellow at the University of Washington, Seattle, USA. Currently, he is Lecturer/Assistant Professor at the Queen's University Belfast, UK, at the Center for Cancer Research and Cell Biology, heading the Computational Biology and Machine Learning Lab. His research interests are in the field of computational biology, machine learning and network medicine.