Advances in Network Complexity

Matthias Dehmer 지음

Wiley-Blackwell 2013년 06월 21일 출간

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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 FrankEmmert-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.

목차

Preface XIList of Contributors XIII1 Functional Complexity Based on Topology 1Hildegard Meyer-Ortmanns1.1 Introduction 11.2 A Measure for the Functional Complexity of Networks 31.2.1 Topological Equivalence of LCE-Graphs 31.2.2 Vertex Resolution Patterns 51.2.3 Kauffman States for Link Invariants 61.2.4 Definition of the Complexity Measure 81.3 Applications 91.3.1 Creation of a Loop 101.3.2 Networks of Information 101.3.3 Transport Networks of Cargo 101.3.4 Boolean Networks of Gene Regulation 121.3.5 Topological Quantum Systems 121.3.6 Steering Dynamics Stored in Knots and Links 131.4 Conclusions 14References 152 Connections Between Artificial Intelligence and Computational Complexity and the Complexity of Graphs 17Angel Garrido2.1 Introduction 172.2 Representation Methods 182.3 Searching Methods 202.4 Turing Machines 222.5 Fuzzy Logic and Fuzzy Graphs 242.6 Fuzzy Optimization 262.7 Fuzzy Systems 272.8 Problems Related to AI 272.9 Topology of Complex Networks 282.10 Hierarchies 302.10.1 Deterministic Case 302.10.2 Nondeterministic Case 312.10.3 Alternating Case 312.11 Graph Entropy 322.12 Kolmogorov Complexity 342.13 Conclusion 37References 383 Selection-Based Estimates of Complexity Unravel Some Mechanisms and Selective Pressures Underlying the Evolution of Complexity in Artificial Networks 41Herve Le Nagard and Olivier Tenaillon3.1 Introduction 413.2 Complexity and Evolution 423.3 Macroscopic Quantification of Organismal Complexity 433.4 Selection-Based Methods of Complexity 443.5 Informational Complexity 443.6 Fisher Geometric Model 463.7 The Cost of Complexity 483.8 Quantifying Phenotypic Complexit

출판사 서평

“Theory and practical applications are intertwined to give the reader a deeper appreciation of the problems and possible solutions. Network complexity is a rapidly evolving field touching on a wide range of issues from pure mathematics, physics and chemistry to industrial processes and consumer behavior. This book satisfies a pressing need for a comprehensive overview of the current state of the field.”& (AMS Journal, 1 October 2013)“Overall, a valuable addition to the literature and a must-have for anyone dealing with complex systems. The articles of this volume will not be reviewed individually.”& (Zentralblatt Math, 1 September &&2013)"In summary, Advances in Network Complexity is a valuable treatise, outlining the many facets of the contemporary approaches to network complexity.... It should be a must for any decent science library." (MATCH Communications in Mathematical and in Computer Chemistry, 1 December 2013)In summary, \Advances in Network Complexity" is a valuable treatise, outliningthe many facets of the contemporary approaches to network complexity. It will beuseful for both experts and beginners. It should be a must for any decent sciencelibrary.

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저자(글) Matthias Dehmer

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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 andUniversity 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, ErasmusUniversity-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 graphtheory 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 theUniversity 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 fi eldof computational biology, machine learning and network medicine.