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Ris. 28/17$g1$i2002000612946$lle002$op$pE29.00$q-$rn$so $t0$u0$v0$w0$x0$y.i1514365x$z18-06-10 945 $aLE016 070.579 MAN 01.01$g1$i2016000072006$lle016$on$pE29.00$q-$rn$so $t0$u0$v0$w0$x0$y.i14680154$z26-02-08 945 $aLE002 070.5 MAN$g2$i2002001091405$lle002$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i15873870$z25-01-19 996 $aInternet nel lavoro editoriale$9722064 997 $aUNISALENTO 998 $ale002$a(2)le016$b08-01-08$cm$da $e-$fita$git $h0$i0 LEADER 05245oam 2200613 450 001 9910815777903321 005 20190911112726.0 010 $a3-527-67048-3 010 $a3-527-67046-7 010 $a3-527-67047-5 035 $a(OCoLC)851153926 035 $a(MiFhGG)GVRL8FKB 035 $a(EXLCZ)992550000001094576 100 $a20140416d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 00$aAdvances in network complexity /$fedited by Matthias Dehmer, Abbe Mowshowitz and Frank Emmert-Streib 210 $aWeinheim $cWiley-Blackwell$dc2013 210 1$aWeinheim [Germany] :$cWiley-Blackwell,$d2013. 215 $a1 online resource (xiv, 293 pages) $cillustrations 225 1 $aQuantitative and network biology ;$vvol. 4 300 $aFormerly CIP.$5Uk 311 $a3-527-33291-X 311 $a1-299-70146-9 320 $aIncludes bibliographical references and index. 327 $aAdvances in Network Complexity; Contents; Preface; List of Contributors; 1 Functional Complexity Based on Topology; 1.1 Introduction; 1.2 A Measure for the Functional Complexity of Networks; 1.2.1 Topological Equivalence of LCE-Graphs; 1.2.2 Vertex Resolution Patterns; 1.2.3 Kauffman States for Link Invariants; 1.2.4 Definition of the Complexity Measure; 1.3 Applications; 1.3.1 Creation of a Loop; 1.3.2 Networks of Information; 1.3.3 Transport Networks of Cargo; 1.3.4 Boolean Networks of Gene Regulation; 1.3.5 Topological Quantum Systems; 1.3.6 Steering Dynamics Stored in Knots and Links 327 $a1.4 ConclusionsReferences; 2 Connections Between Artificial Intelligence and Computational Complexity and the Complexity of Graphs; 2.1 Introduction; 2.2 Representation Methods; 2.3 Searching Methods; 2.4 Turing Machines; 2.5 Fuzzy Logic and Fuzzy Graphs; 2.6 Fuzzy Optimization; 2.7 Fuzzy Systems; 2.8 Problems Related to AI; 2.9 Topology of Complex Networks; 2.10 Hierarchies; 2.10.1 Deterministic Case; 2.10.2 Nondeterministic Case; 2.10.3 Alternating Case; 2.11 Graph Entropy; 2.12 Kolmogorov Complexity; 2.13 Conclusion; References 327 $a3 Selection-Based Estimates of Complexity Unravel Some Mechanisms and Selective Pressures Underlying the Evolution of Complexity in Artificial Networks3.1 Introduction; 3.2 Complexity and Evolution; 3.3 Macroscopic Quantification of Organismal Complexity; 3.4 Selection-Based Methods of Complexity; 3.5 Informational Complexity; 3.6 Fisher Geometric Model; 3.7 The Cost of Complexity; 3.8 Quantifying Phenotypic Complexity; 3.8.1 Mutation-Based Method: Mutational Phenotypic Complexity (MPC); 3.8.2 Drift Load Based Method: Effective Phenotypic Complexity (EPC) 327 $a3.8.3 Statistical Method: Principal Component Phenotypic Complexity (PCPC)3.9 Darwinian Adaptive Neural Networks (DANN); 3.10 The Different Facets of Complexity; 3.11 Mechanistic Understanding of Phenotypic Complexity; 3.12 Selective Pressures Acting on Phenotypic Complexity; 3.13 Conclusion and Perspectives; References; 4 Three Types of Network Complexity Pyramid; 4.1 Introduction; 4.2 The First Type: The Life's Complexity Pyramid (LCP); 4.3 The Second Type: Network Model Complexity Pyramid; 4.3.1 The Level-7: Euler (Regular) Graphs; 4.3.2 The Level-6: Erd?os-R enyi Random Graph 327 $a4.3.3 The Level-5: Small-World Network and Scale-Free Models4.3.4 The Level-4: Weighted Evolving Network Models; 4.3.5 The Bottom Three Levels of the NMCP; 4.3.5.1 The Level-3: The HUHPNM; 4.3.5.2 The Level-2: The LUHNM; 4.3.5.3 The Level-1: The LUHNM-VSG; 4.4 The Third Type: Generalized Farey Organized Network Pyramid; 4.4.1 Construction Method of the Generalized Farey Tree Network (GFTN); 4.4.2 Main Results of the GFTN; 4.4.2.1 Degree Distribution; 4.4.2.2 Clustering Coefficient; 4.4.2.3 Diameter and Small World; 4.4.2.4 Degree-Degree Correlations; 4.4.3 Weighted Property of GFTN 327 $a4.4.4 Generalized Farey Organized Network Pyramid (GFONP) 330 $aA well-balanced overview of mathematical approaches to complex systems ranging from applications in chemistry and ecology to basic research questions on network complexity. Matthias Dehmer, Abbe Mowshowitz, and FrankEmmert-Streib, well-known pioneers in the fi eld, have edited this volume with a view to balancing classical and modern approaches to ensure broad coverage of contemporary research problems.The book is a valuable addition to the literature and a must-have for anyone dealing with network compleaity and complexity issues. 410 0$aQuantitative and network biology ;$vv. 4. 606 $aSystem analysis 606 $aComputational complexity 606 $aNetwork analysis (Planning)$xMathematical models 606 $aGraph theory 615 0$aSystem analysis. 615 0$aComputational complexity. 615 0$aNetwork analysis (Planning)$xMathematical models. 615 0$aGraph theory. 676 $a003.72 702 $aDehmer$b Matthias$f1968- 702 $aMowshowitz$b Abbe 702 $aEmmert-Streib$b Frank 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910815777903321 996 $aAdvances in network complexity$94084940 997 $aUNINA