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Advances in network complexity / / edited by Matthias Dehmer, Abbe Mowshowitz and Frank Emmert-Streib
| Advances in network complexity / / edited by Matthias Dehmer, Abbe Mowshowitz and Frank Emmert-Streib |
| Pubbl/distr/stampa | Weinheim, : Wiley-Blackwell, c2013 |
| Descrizione fisica | 1 online resource (xiv, 293 pages) : illustrations |
| Disciplina | 003.72 |
| Collana | Quantitative and network biology |
| Soggetto topico |
System analysis
Computational complexity Network analysis (Planning) - Mathematical models Graph theory |
| ISBN |
3-527-67048-3
3-527-67046-7 3-527-67047-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advances 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
1.4 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 3 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) 3.8.3 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 4.3.3 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 4.4.4 Generalized Farey Organized Network Pyramid (GFONP) |
| Record Nr. | UNINA-9910139042303321 |
| Weinheim, : Wiley-Blackwell, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in network complexity / / edited by Matthias Dehmer, Abbe Mowshowitz and Frank Emmert-Streib
| Advances in network complexity / / edited by Matthias Dehmer, Abbe Mowshowitz and Frank Emmert-Streib |
| Pubbl/distr/stampa | Weinheim, : Wiley-Blackwell, c2013 |
| Descrizione fisica | 1 online resource (xiv, 293 pages) : illustrations |
| Disciplina | 003.72 |
| Collana | Quantitative and network biology |
| Soggetto topico |
System analysis
Computational complexity Network analysis (Planning) - Mathematical models Graph theory |
| ISBN |
3-527-67048-3
3-527-67046-7 3-527-67047-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advances 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
1.4 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 3 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) 3.8.3 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 4.3.3 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 4.4.4 Generalized Farey Organized Network Pyramid (GFONP) |
| Record Nr. | UNINA-9910815777903321 |
| Weinheim, : Wiley-Blackwell, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib
| Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib |
| Autore | Dehmer Matthias |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH, c2009 |
| Descrizione fisica | 1 online resource (482 p.) |
| Disciplina | 515 |
| Altri autori (Persone) |
DehmerMatthias <1968->
Emmert-StreibFrank |
| Soggetto topico |
Mathematical analysis
Information networks Graph theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-68269-5
9786612682698 3-527-62798-7 3-527-62799-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Complex Networks From Biology to Linguistics; Contents; Preface; List of Contributors; 1 Entropy, Orbits, and Spectra of Graphs; 1.1 Introduction; 1.2 Entropy or the Information Content of Graphs; 1.3 Groups and Graph Spectra; 1.4 Approximating Orbits; 1.4.1 The Degree of the Vertices; 1.4.2 The Point-Deleted Neighborhood Degree Vector; 1.4.3 Betweenness Centrality; 1.5 Alternative Bases for Structural Complexity; References; 2 Statistical Mechanics of Complex Networks; 2.1 Introduction; 2.1.1 Network Entropies; 2.1.2 Network Hamiltonians; 2.1.3 Network Ensembles
2.1.4 Some Definitions of Network Measures2.2 Macroscopics: Entropies for Networks; 2.2.1 A General Set of Network Models Maximizing Generalized Entropies; 2.2.1.1 A Unified Network Model; 2.2.1.2 Famous Limits of the Unified Model; 2.2.1.3 Unified Model: Additional Features; 2.3 Microscopics: Hamiltonians of Networks - Network Thermodynamics; 2.3.1 Topological Phase Transitions; 2.3.2 A Note on Entropy; 2.4 Ensembles of Random Networks - Superstatistics; 2.5 Conclusion; References; 3 A Simple Integrated Approach to Network Complexity and Node Centrality; 3.1 Introduction 3.2 The Small-World Connectivity Descriptors3.3 The Integrated Centrality Measure; References; 4 Spectral Theory of Networks: From Biomolecular to Ecological Systems; 4.1 Introduction; 4.2 Background on Graph Spectra; 4.3 Spectral Measures of Node Centrality; 4.3.1 Subgraph Centrality as a Partition Function; 4.3.2 Application; 4.4 Global Topological Organization of Complex Networks; 4.4.1 Spectral Scaling Method; 4.4.2 Universal Topological Classes of Networks; 4.4.3 Applications; 4.5 Communicability in Complex Networks; 4.5.1 Communicability and Network Communities 4.5.2 Detection of Communities: The Communicability Graph4.5.3 Application; 4.6 Network Bipartivity; 4.6.1 Detecting Bipartite Substructures in Complex Networks; 4.6.2 Application; 4.7 Conclusion; References; 5 On the Structure of Neutral Networks of RNA Pseudoknot Structures; 5.1 Motivation and Background; 5.1.1 Notation and Terminology; 5.2 Preliminaries; 5.3 Connectivity; 5.4 The Largest Component; 5.5 Distances in n-Cubes; 5.6 Conclusion; References; 6 Graph Edit Distance - Optimal and Suboptimal Algorithms with Applications; 6.1 Introduction; 6.2 Graph Edit Distance 6.3 Computation of GED6.3.1 Optimal Algorithms; 6.3.2 Suboptimal Algorithms; 6.3.2.1 Bipartite Graph Matching; 6.4 Applications; 6.4.1 Graph Data Sets; 6.4.2 GED-Based Nearest-Neighbor Classification; 6.4.3 Dissimilarity-Based Embedding Graph Kernels; 6.5 Experimental Evaluation; 6.5.1 Optimal vs. Suboptimal Graph Edit Distance; 6.5.2 Dissimilarity Embedding Graph Kernels Based on Suboptimal Graph Edit Distance; 6.6 Summary and Conclusions; References; 7 Graph Energy; 7.1 Introduction; 7.2 Bounds for the Energy of Graphs; 7.2.1 Some Upper Bounds; 7.2.2 Some Lower Bounds 7.3 Hyperenergetic, Hypoenergetic, and Equienergetic Graphs |
| Record Nr. | UNINA-9910139802803321 |
Dehmer Matthias
|
||
| Weinheim, : Wiley-VCH, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib
| Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib |
| Autore | Dehmer Matthias |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH, c2009 |
| Descrizione fisica | 1 online resource (482 p.) |
| Disciplina | 515 |
| Altri autori (Persone) |
DehmerMatthias <1968->
Emmert-StreibFrank |
| Soggetto topico |
Mathematical analysis
Information networks Graph theory |
| ISBN |
1-282-68269-5
9786612682698 3-527-62798-7 3-527-62799-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Complex Networks From Biology to Linguistics; Contents; Preface; List of Contributors; 1 Entropy, Orbits, and Spectra of Graphs; 1.1 Introduction; 1.2 Entropy or the Information Content of Graphs; 1.3 Groups and Graph Spectra; 1.4 Approximating Orbits; 1.4.1 The Degree of the Vertices; 1.4.2 The Point-Deleted Neighborhood Degree Vector; 1.4.3 Betweenness Centrality; 1.5 Alternative Bases for Structural Complexity; References; 2 Statistical Mechanics of Complex Networks; 2.1 Introduction; 2.1.1 Network Entropies; 2.1.2 Network Hamiltonians; 2.1.3 Network Ensembles
2.1.4 Some Definitions of Network Measures2.2 Macroscopics: Entropies for Networks; 2.2.1 A General Set of Network Models Maximizing Generalized Entropies; 2.2.1.1 A Unified Network Model; 2.2.1.2 Famous Limits of the Unified Model; 2.2.1.3 Unified Model: Additional Features; 2.3 Microscopics: Hamiltonians of Networks - Network Thermodynamics; 2.3.1 Topological Phase Transitions; 2.3.2 A Note on Entropy; 2.4 Ensembles of Random Networks - Superstatistics; 2.5 Conclusion; References; 3 A Simple Integrated Approach to Network Complexity and Node Centrality; 3.1 Introduction 3.2 The Small-World Connectivity Descriptors3.3 The Integrated Centrality Measure; References; 4 Spectral Theory of Networks: From Biomolecular to Ecological Systems; 4.1 Introduction; 4.2 Background on Graph Spectra; 4.3 Spectral Measures of Node Centrality; 4.3.1 Subgraph Centrality as a Partition Function; 4.3.2 Application; 4.4 Global Topological Organization of Complex Networks; 4.4.1 Spectral Scaling Method; 4.4.2 Universal Topological Classes of Networks; 4.4.3 Applications; 4.5 Communicability in Complex Networks; 4.5.1 Communicability and Network Communities 4.5.2 Detection of Communities: The Communicability Graph4.5.3 Application; 4.6 Network Bipartivity; 4.6.1 Detecting Bipartite Substructures in Complex Networks; 4.6.2 Application; 4.7 Conclusion; References; 5 On the Structure of Neutral Networks of RNA Pseudoknot Structures; 5.1 Motivation and Background; 5.1.1 Notation and Terminology; 5.2 Preliminaries; 5.3 Connectivity; 5.4 The Largest Component; 5.5 Distances in n-Cubes; 5.6 Conclusion; References; 6 Graph Edit Distance - Optimal and Suboptimal Algorithms with Applications; 6.1 Introduction; 6.2 Graph Edit Distance 6.3 Computation of GED6.3.1 Optimal Algorithms; 6.3.2 Suboptimal Algorithms; 6.3.2.1 Bipartite Graph Matching; 6.4 Applications; 6.4.1 Graph Data Sets; 6.4.2 GED-Based Nearest-Neighbor Classification; 6.4.3 Dissimilarity-Based Embedding Graph Kernels; 6.5 Experimental Evaluation; 6.5.1 Optimal vs. Suboptimal Graph Edit Distance; 6.5.2 Dissimilarity Embedding Graph Kernels Based on Suboptimal Graph Edit Distance; 6.6 Summary and Conclusions; References; 7 Graph Energy; 7.1 Introduction; 7.2 Bounds for the Energy of Graphs; 7.2.1 Some Upper Bounds; 7.2.2 Some Lower Bounds 7.3 Hyperenergetic, Hypoenergetic, and Equienergetic Graphs |
| Record Nr. | UNINA-9910830301503321 |
|
Dehmer Matthias
|
||
| Weinheim, : Wiley-VCH, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib
| Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib |
| Autore | Dehmer Matthias |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH, c2009 |
| Descrizione fisica | 1 online resource (482 p.) |
| Disciplina | 515 |
| Altri autori (Persone) |
DehmerMatthias <1968->
Emmert-StreibFrank |
| Soggetto topico |
Mathematical analysis
Information networks Graph theory |
| ISBN |
1-282-68269-5
9786612682698 3-527-62798-7 3-527-62799-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Complex Networks From Biology to Linguistics; Contents; Preface; List of Contributors; 1 Entropy, Orbits, and Spectra of Graphs; 1.1 Introduction; 1.2 Entropy or the Information Content of Graphs; 1.3 Groups and Graph Spectra; 1.4 Approximating Orbits; 1.4.1 The Degree of the Vertices; 1.4.2 The Point-Deleted Neighborhood Degree Vector; 1.4.3 Betweenness Centrality; 1.5 Alternative Bases for Structural Complexity; References; 2 Statistical Mechanics of Complex Networks; 2.1 Introduction; 2.1.1 Network Entropies; 2.1.2 Network Hamiltonians; 2.1.3 Network Ensembles
2.1.4 Some Definitions of Network Measures2.2 Macroscopics: Entropies for Networks; 2.2.1 A General Set of Network Models Maximizing Generalized Entropies; 2.2.1.1 A Unified Network Model; 2.2.1.2 Famous Limits of the Unified Model; 2.2.1.3 Unified Model: Additional Features; 2.3 Microscopics: Hamiltonians of Networks - Network Thermodynamics; 2.3.1 Topological Phase Transitions; 2.3.2 A Note on Entropy; 2.4 Ensembles of Random Networks - Superstatistics; 2.5 Conclusion; References; 3 A Simple Integrated Approach to Network Complexity and Node Centrality; 3.1 Introduction 3.2 The Small-World Connectivity Descriptors3.3 The Integrated Centrality Measure; References; 4 Spectral Theory of Networks: From Biomolecular to Ecological Systems; 4.1 Introduction; 4.2 Background on Graph Spectra; 4.3 Spectral Measures of Node Centrality; 4.3.1 Subgraph Centrality as a Partition Function; 4.3.2 Application; 4.4 Global Topological Organization of Complex Networks; 4.4.1 Spectral Scaling Method; 4.4.2 Universal Topological Classes of Networks; 4.4.3 Applications; 4.5 Communicability in Complex Networks; 4.5.1 Communicability and Network Communities 4.5.2 Detection of Communities: The Communicability Graph4.5.3 Application; 4.6 Network Bipartivity; 4.6.1 Detecting Bipartite Substructures in Complex Networks; 4.6.2 Application; 4.7 Conclusion; References; 5 On the Structure of Neutral Networks of RNA Pseudoknot Structures; 5.1 Motivation and Background; 5.1.1 Notation and Terminology; 5.2 Preliminaries; 5.3 Connectivity; 5.4 The Largest Component; 5.5 Distances in n-Cubes; 5.6 Conclusion; References; 6 Graph Edit Distance - Optimal and Suboptimal Algorithms with Applications; 6.1 Introduction; 6.2 Graph Edit Distance 6.3 Computation of GED6.3.1 Optimal Algorithms; 6.3.2 Suboptimal Algorithms; 6.3.2.1 Bipartite Graph Matching; 6.4 Applications; 6.4.1 Graph Data Sets; 6.4.2 GED-Based Nearest-Neighbor Classification; 6.4.3 Dissimilarity-Based Embedding Graph Kernels; 6.5 Experimental Evaluation; 6.5.1 Optimal vs. Suboptimal Graph Edit Distance; 6.5.2 Dissimilarity Embedding Graph Kernels Based on Suboptimal Graph Edit Distance; 6.6 Summary and Conclusions; References; 7 Graph Energy; 7.1 Introduction; 7.2 Bounds for the Energy of Graphs; 7.2.1 Some Upper Bounds; 7.2.2 Some Lower Bounds 7.3 Hyperenergetic, Hypoenergetic, and Equienergetic Graphs |
| Record Nr. | UNINA-9910840815503321 |
|
Dehmer Matthias
|
||
| Weinheim, : Wiley-VCH, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Quantitative graph theory : mathematical foundations and applications / / edited by Matthias Dehmer, Institute for Theoretical Computer Science, Mathematics and Operations Research, Department of Computer Science, Universitat der Bundeswehr Munc
| Quantitative graph theory : mathematical foundations and applications / / edited by Matthias Dehmer, Institute for Theoretical Computer Science, Mathematics and Operations Research, Department of Computer Science, Universitat der Bundeswehr Munc |
| Pubbl/distr/stampa | Boca Raton : , : CRC Press, , [2015] |
| Descrizione fisica | 1 online resource (516 p.) |
| Disciplina | 511.5 |
| Collana | Discrete mathematics and its applications |
| Soggetto topico |
Graph theory - Data processing
Combinatorial analysis |
| ISBN |
0-429-10326-3
1-4665-8452-1 |
| Classificazione | COM046000MAT036000SCI008000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Dedication; Contents; Preface; Editors; Contributors; Chapter 1 What Is Quantitative Graph Theory?; Chapter 2 Localization of Graph Topological Indices via Majorization Technique; Chapter 3 Wiener Index of Hexagonal Chains with Segments of Equal Length; Chapter 4 Metric-Extremal Graphs; Chapter 5 Quantitative Methods for Nowhere-Zero Flows and Edge Colorings; Chapter 6 Width-Measures for Directed Graphs and Algorithmic Applications; Chapter 7 Betweenness Centrality in Graphs; Chapter 8 On a Variant Szeged and PI* Indices of Thorn Graphs; Chapter 9 Wiener Index of Line Graphs
Chapter 10 Single-Graph Support MeasuresChapter 11 Network Sampling Algorithms and Applications; Chapter 12 Discrimination of Image Textures Using Graph Indices; Chapter 13 Network Analysis Applied to the Political Networks of Mexico; Chapter 14 Social Network Centrality, MovementIdentification, and the Participation ofIndividuals in a Social Movement: The Case of the Canadian Environmental Movement; Chapter 15 Graph Kernels in Chemoinformatics; Chapter 16 Chemical Compound Complexity in Biological Pathways; Back Cover |
| Record Nr. | UNINA-9910787301403321 |
| Boca Raton : , : CRC Press, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Quantitative graph theory : mathematical foundations and applications / / edited by Matthias Dehmer, Institute for Theoretical Computer Science, Mathematics and Operations Research, Department of Computer Science, Universitat der Bundeswehr Munc
| Quantitative graph theory : mathematical foundations and applications / / edited by Matthias Dehmer, Institute for Theoretical Computer Science, Mathematics and Operations Research, Department of Computer Science, Universitat der Bundeswehr Munc |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boca Raton : , : CRC Press, , [2015] |
| Descrizione fisica | 1 online resource (516 p.) |
| Disciplina | 511.5 |
| Collana | Discrete mathematics and its applications |
| Soggetto topico |
Graph theory - Data processing
Combinatorial analysis |
| ISBN |
0-429-10326-3
1-4665-8452-1 |
| Classificazione | COM046000MAT036000SCI008000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Dedication; Contents; Preface; Editors; Contributors; Chapter 1 What Is Quantitative Graph Theory?; Chapter 2 Localization of Graph Topological Indices via Majorization Technique; Chapter 3 Wiener Index of Hexagonal Chains with Segments of Equal Length; Chapter 4 Metric-Extremal Graphs; Chapter 5 Quantitative Methods for Nowhere-Zero Flows and Edge Colorings; Chapter 6 Width-Measures for Directed Graphs and Algorithmic Applications; Chapter 7 Betweenness Centrality in Graphs; Chapter 8 On a Variant Szeged and PI* Indices of Thorn Graphs; Chapter 9 Wiener Index of Line Graphs
Chapter 10 Single-Graph Support MeasuresChapter 11 Network Sampling Algorithms and Applications; Chapter 12 Discrimination of Image Textures Using Graph Indices; Chapter 13 Network Analysis Applied to the Political Networks of Mexico; Chapter 14 Social Network Centrality, MovementIdentification, and the Participation ofIndividuals in a Social Movement: The Case of the Canadian Environmental Movement; Chapter 15 Graph Kernels in Chemoinformatics; Chapter 16 Chemical Compound Complexity in Biological Pathways; Back Cover |
| Record Nr. | UNINA-9910829069303321 |
| Boca Raton : , : CRC Press, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical and machine learning approaches for network analysis / / edited by Matthias Dehmer, Subhash C. Basak
| Statistical and machine learning approaches for network analysis / / edited by Matthias Dehmer, Subhash C. Basak |
| Autore | Dehmer Matthias <1968-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2012 |
| Descrizione fisica | 1 online resource (345 p.) |
| Disciplina | 511/.5 |
| Altri autori (Persone) |
DehmerMatthias <1968->
BasakSubhash C. <1945-> |
| Collana | Wiley series in computational statistics |
| Soggetto topico |
Research - Statistical methods
Machine theory Communication - Network analysis - Graphic methods Information science - Statistical methods |
| ISBN |
1-280-87271-3
9786613714022 1-118-34698-X 1-118-34699-8 1-118-34701-3 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistical and Machine Learning Approaches for Network Analysis; Contents; Preface; Contributors; 1 A Survey of Computational Approaches to Reconstruct and Partition Biological Networks; 1.1 INTRODUCTION; 1.2 BIOLOGICAL NETWORKS; 1.2.1 Directed Networks; 1.2.2 Undirected Networks; 1.3 GENOME-WIDE MEASUREMENTS; 1.3.1 Gene Expression Data; 1.3.2 Gene Sets; 1.4 RECONSTRUCTION OF BIOLOGICAL NETWORKS; 1.4.1 Reconstruction of Directed Networks; 1.4.1.1 Boolean Networks; 1.4.1.2 Probabilistic Boolean Networks; 1.4.1.3 Bayesian Networks; 1.4.1.4 Collaborative Graph Model; 1.4.1.5 Frequency Method
1.4.1.6 EM-Based Inference from Gene Sets1.4.2 Reconstruction of Undirected Networks; 1.4.2.1 Relevance Networks; 1.4.2.2 Graphical Gaussian Models; 1.5 PARTITIONING BIOLOGICAL NETWORKS; 1.5.1 Directed and Undirected Networks; 1.5.2 Partitioning Undirected Networks; 1.5.2.1 Kernighan-Lin Algorithm; 1.5.2.2 Girvan-Newman Algorithm; 1.5.2.3 Newman's Eigenvector Method; 1.5.2.4 Infomap; 1.5.2.5 Clique Percolation Method; 1.5.3 Partitioning Directed Networks; 1.5.3.1 Newman's Eigenvector Method; 1.5.3.2 Infomap; 1.5.3.3 Clique Percolation Method; 1.6 DISCUSSION; REFERENCES 2 Introduction to Complex Networks: Measures, Statistical Properties, and Models2.1 INTRODUCTION; 2.2 REPRESENTATION OF NETWORKS; 2.3 CLASSICAL NETWORK; 2.3.1 Random Network; 2.3.2 Lattice Network; 2.4 SCALE-FREE NETWORK; 2.4.1 Degree Distribution; 2.4.2 Degree Distribution of Random Network; 2.4.3 Power-Law Distribution in Real-World Networks; 2.4.4 Barab ́asi-Albert Model; 2.4.5 Configuration Model; 2.5 SMALL-WORLD NETWORK; 2.5.1 Average Shortest Path Length; 2.5.2 Ultrasmall-World Network; 2.6 CLUSTERED NETWORK; 2.6.1 Clustering Coefficient; 2.6.2 Watts-Strogatz Model 2.7 HIERARCHICAL MODULARITY2.7.1 Hierarchical Model; 2.7.2 Dorogovtsev-Mendes-Samukhin Model; 2.8 NETWORK MOTIF; 2.9 ASSORTATIVITY; 2.9.1 Assortative Coefficient; 2.9.2 Degree Correlation; 2.9.3 Linear Preferential Attachment Model; 2.9.4 Edge Rewiring Method; 2.10 RECIPROCITY; 2.11 WEIGHTED NETWORKS; 2.11.1 Strength; 2.11.2 Weighted Clustering Coefficient; 2.11.3 Weighted Degree Correlation; 2.12 NETWORK COMPLEXITY; 2.13 CENTRALITY; 2.13.1 Definition; 2.13.2 Comparison of Centrality Measures; 2.14 CONCLUSION; REFERENCES; 3 Modeling for Evolving Biological Networks; 3.1 INTRODUCTION 3.2 UNIFIED EVOLVING NETWORK MODEL: REPRODUCTION OF HETEROGENEOUS CONNECTIVITY, HIERARCHICAL MODULARITY, AND DISASSORTATIVITY3.2.1 Network Model; 3.2.2 Degree Distribution; 3.2.3 Degree-Dependent Clustering Coefficient; 3.2.4 Average Clustering Coefficient; 3.2.5 Degree Correlation; 3.2.6 Assortative Coefficient; 3.2.7 Comparison with Real Data; 3.3 MODELING WITHOUT PARAMETER TUNING: A CASE STUDY OF METABOLIC NETWORKS; 3.3.1 Network Model; 3.3.2 Analytical Solution; 3.3.3 Estimation of the Parameters; 3.3.4 Comparison with Real Data 3.4 BIPARTITE RELATIONSHIP: A CASE STUDY OF METABOLITE DISTRIBUTION |
| Record Nr. | UNINA-9910141263803321 |
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Dehmer Matthias <1968->
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| Hoboken, N.J., : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Statistical diagnostics for cancer [[electronic resource] ] : analyzing high-dimensional data / / edited by Frank Emmert-Streib and Matthias Dehmer
| Statistical diagnostics for cancer [[electronic resource] ] : analyzing high-dimensional data / / edited by Frank Emmert-Streib and Matthias Dehmer |
| Pubbl/distr/stampa | Weinheim, : Wiley-Blackwell, 2013 |
| Descrizione fisica | 1 online resource (314 p.) |
| Disciplina | 616.994075 |
| Altri autori (Persone) |
Emmert-StreibFrank
DehmerMatthias <1968-> |
| Collana |
Quantitative and Network Biology (VCH)
Quantitative and network biology |
| Soggetto topico | Cancer - Diagnosis |
| Soggetto genere / forma | Electronic books. |
| ISBN |
3-527-66547-1
1-299-15851-X 3-527-66544-7 3-527-66545-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. General overview -- pt. 2. Bayesian methods -- pt. 3. Network-based approaches -- pt. 4. Phenotype influence of DNA copy number aberrations. |
| Record Nr. | UNINA-9910141464803321 |
| Weinheim, : Wiley-Blackwell, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical diagnostics for cancer : analyzing high-dimensional data / / edited by Frank Emmert-Streib and Matthias Dehmer
| Statistical diagnostics for cancer : analyzing high-dimensional data / / edited by Frank Emmert-Streib and Matthias Dehmer |
| Pubbl/distr/stampa | Weinheim, : Wiley-Blackwell, 2013 |
| Descrizione fisica | 1 online resource (xx, 292 pages) : illustrations (some color) |
| Disciplina | 616.994075 |
| Collana |
Quantitative and Network Biology (VCH)
Quantitative and network biology |
| Soggetto topico |
Cancer - Diagnosis - Statistical methods
Cancer - Genetic aspects - Statistical methods |
| ISBN |
3-527-66547-1
1-299-15851-X 3-527-66544-7 3-527-66545-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. 1. General overview -- pt. 2. Bayesian methods -- pt. 3. Network-based approaches -- pt. 4. Phenotype influence of DNA copy number aberrations. |
| Record Nr. | UNINA-9910830855103321 |
| Weinheim, : Wiley-Blackwell, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
