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Computational network theory : theoretical foundations and applications / / Edited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl
| Computational network theory : theoretical foundations and applications / / Edited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl |
| Pubbl/distr/stampa | Weinheim, Germany : , : Wiley-VCH Verlang GmbH & Co. KGaA, , 2015 |
| Descrizione fisica | 1 online resource (281 p.) |
| Disciplina | 006.3 |
| Collana | Quantitative and network biology |
| Soggetto topico |
Electronic commerce
Computational intelligence |
| Soggetto genere / forma | Electronic books. |
| ISBN |
3-527-69154-5
3-527-69151-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Dedication; Contents; Color Plates; Preface; List of Contributors; Chapter 1 Model Selection for Neural Network Models: A Statistical Perspective; 1.1 Introduction; 1.2 Feedforward Neural Network Models; 1.3 Model Selection; 1.3.1 Feature Selection by Relevance Measures; 1.3.2 Some Numerical Examples; 1.3.3 Application to Real Data; 1.4 The Selection of the Hidden Layer Size; 1.4.1 A Reality Check Approach; 1.4.2 Numerical Examples by Using the Reality Check; 1.4.3 Testing Superior Predictive Ability for Neural Network Modeling
1.4.4 Some Numerical Results Using Test of Superior Predictive Ability1.4.5 An Application to Real Data; 1.5 Concluding Remarks; References; Chapter 2 Measuring Structural Correlations in Graphs; 2.1 Introduction; 2.1.1 Solutions for Measuring Structural Correlations; 2.2 Related Work; 2.3 Self Structural Correlation; 2.3.1 Problem Formulation; 2.3.2 The Measure; 2.3.2.1 Random Walk and Hitting Time; 2.3.2.2 Decayed Hitting Time; 2.3.3 Computing Decayed Hitting Time; 2.3.3.1 Iterative Approximation; 2.3.3.2 A Sampling Algorithm for h(vi,B); 2.3.3.3 Complexity; 2.3.4 Assessing SSC 2.3.4.1 Estimating ρ (Vq)2.3.4.2 Estimating the Significance of ρ (Vq); 2.3.5 Empirical Studies; 2.3.5.1 Datasets; 2.3.5.2 Performance of DHT Approximation; 2.3.5.3 Effectiveness on Synthetic Events; 2.3.5.4 SSC of Real Event; 2.3.5.5 Scalability of Sampling-alg; 2.3.6 Discussions; 2.4 Two-Event Structural Correlation; 2.4.1 Preliminaries and Problem Formulation; 2.4.2 Measuring TESC; 2.4.2.1 The Test; 2.4.2.2 Reference Nodes; 2.4.3 Reference Node Sampling; 2.4.3.1 Batch_BFS; 2.4.3.2 Importance Sampling; 2.4.3.3 Global Sampling in Whole Graph; 2.4.3.4 Complexity Analysis; 2.4.4 Experiments 2.4.4.1 Graph Datasets2.4.4.2 Event Simulation Methodology; 2.4.4.3 Performance Comparison; 2.4.4.4 Batch Importance Sampling; 2.4.4.5 Impact of Graph Density; 2.4.4.6 Efficiency and Scalability; 2.4.4.7 Real Events; 2.4.5 Discussions; 2.5 Conclusions; Acknowledgments; References; Chapter 3 Spectral Graph Theory and Structural Analysis of Complex Networks: An Introduction; 3.1 Introduction; 3.2 Graph Theory: Some Basic Concepts; 3.2.1 Connectivity in Graphs; 3.2.2 Subgraphs and Special Graphs; 3.3 Matrix Theory: Some Basic Concepts; 3.3.1 Trace and Determinant of a Matrix 3.3.2 Eigenvalues and Eigenvectors of a Matrix3.4 Graph Matrices; 3.4.1 Adjacency Matrix; 3.4.2 Incidence Matrix; 3.4.3 Degree Matrix and Diffusion Matrix; 3.4.4 Laplace Matrix; 3.4.5 Cut-Set Matrix; 3.4.6 Path Matrix; 3.5 Spectral Graph Theory: Some Basic Results; 3.5.1 Spectral Characterization of Graph Connectivity; 3.5.1.1 Spectral Theory and Walks; 3.5.2 Spectral Characteristics of some Special Graphs and Subgraphs; 3.5.2.1 Tree; 3.5.2.2 Bipartite Graph; 3.5.2.3 Complete Graph; 3.5.2.4 Regular Graph; 3.5.2.5 Line Graph; 3.5.3 Spectral Theory and Graph Colouring 3.5.4 Spectral Theory and Graph Drawing |
| Record Nr. | UNINA-9910463483103321 |
| Weinheim, Germany : , : Wiley-VCH Verlang GmbH & Co. KGaA, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational network theory : theoretical foundations and applications / / Edited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl
| Computational network theory : theoretical foundations and applications / / Edited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl |
| Pubbl/distr/stampa | Weinheim, Germany : , : Wiley-VCH Verlang GmbH & Co. KGaA, , 2015 |
| Descrizione fisica | 1 online resource (281 p.) |
| Disciplina | 006.3 |
| Collana | Quantitative and network biology |
| Soggetto topico |
Electronic commerce
Computational intelligence |
| ISBN |
3-527-69154-5
3-527-69151-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Dedication; Contents; Color Plates; Preface; List of Contributors; Chapter 1 Model Selection for Neural Network Models: A Statistical Perspective; 1.1 Introduction; 1.2 Feedforward Neural Network Models; 1.3 Model Selection; 1.3.1 Feature Selection by Relevance Measures; 1.3.2 Some Numerical Examples; 1.3.3 Application to Real Data; 1.4 The Selection of the Hidden Layer Size; 1.4.1 A Reality Check Approach; 1.4.2 Numerical Examples by Using the Reality Check; 1.4.3 Testing Superior Predictive Ability for Neural Network Modeling
1.4.4 Some Numerical Results Using Test of Superior Predictive Ability1.4.5 An Application to Real Data; 1.5 Concluding Remarks; References; Chapter 2 Measuring Structural Correlations in Graphs; 2.1 Introduction; 2.1.1 Solutions for Measuring Structural Correlations; 2.2 Related Work; 2.3 Self Structural Correlation; 2.3.1 Problem Formulation; 2.3.2 The Measure; 2.3.2.1 Random Walk and Hitting Time; 2.3.2.2 Decayed Hitting Time; 2.3.3 Computing Decayed Hitting Time; 2.3.3.1 Iterative Approximation; 2.3.3.2 A Sampling Algorithm for h(vi,B); 2.3.3.3 Complexity; 2.3.4 Assessing SSC 2.3.4.1 Estimating ρ (Vq)2.3.4.2 Estimating the Significance of ρ (Vq); 2.3.5 Empirical Studies; 2.3.5.1 Datasets; 2.3.5.2 Performance of DHT Approximation; 2.3.5.3 Effectiveness on Synthetic Events; 2.3.5.4 SSC of Real Event; 2.3.5.5 Scalability of Sampling-alg; 2.3.6 Discussions; 2.4 Two-Event Structural Correlation; 2.4.1 Preliminaries and Problem Formulation; 2.4.2 Measuring TESC; 2.4.2.1 The Test; 2.4.2.2 Reference Nodes; 2.4.3 Reference Node Sampling; 2.4.3.1 Batch_BFS; 2.4.3.2 Importance Sampling; 2.4.3.3 Global Sampling in Whole Graph; 2.4.3.4 Complexity Analysis; 2.4.4 Experiments 2.4.4.1 Graph Datasets2.4.4.2 Event Simulation Methodology; 2.4.4.3 Performance Comparison; 2.4.4.4 Batch Importance Sampling; 2.4.4.5 Impact of Graph Density; 2.4.4.6 Efficiency and Scalability; 2.4.4.7 Real Events; 2.4.5 Discussions; 2.5 Conclusions; Acknowledgments; References; Chapter 3 Spectral Graph Theory and Structural Analysis of Complex Networks: An Introduction; 3.1 Introduction; 3.2 Graph Theory: Some Basic Concepts; 3.2.1 Connectivity in Graphs; 3.2.2 Subgraphs and Special Graphs; 3.3 Matrix Theory: Some Basic Concepts; 3.3.1 Trace and Determinant of a Matrix 3.3.2 Eigenvalues and Eigenvectors of a Matrix3.4 Graph Matrices; 3.4.1 Adjacency Matrix; 3.4.2 Incidence Matrix; 3.4.3 Degree Matrix and Diffusion Matrix; 3.4.4 Laplace Matrix; 3.4.5 Cut-Set Matrix; 3.4.6 Path Matrix; 3.5 Spectral Graph Theory: Some Basic Results; 3.5.1 Spectral Characterization of Graph Connectivity; 3.5.1.1 Spectral Theory and Walks; 3.5.2 Spectral Characteristics of some Special Graphs and Subgraphs; 3.5.2.1 Tree; 3.5.2.2 Bipartite Graph; 3.5.2.3 Complete Graph; 3.5.2.4 Regular Graph; 3.5.2.5 Line Graph; 3.5.3 Spectral Theory and Graph Colouring 3.5.4 Spectral Theory and Graph Drawing |
| Record Nr. | UNINA-9910788289203321 |
| Weinheim, Germany : , : Wiley-VCH Verlang GmbH & Co. KGaA, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational network theory : theoretical foundations and applications / / Edited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl
| Computational network theory : theoretical foundations and applications / / Edited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl |
| Pubbl/distr/stampa | Weinheim, Germany : , : Wiley-VCH Verlang GmbH & Co. KGaA, , 2015 |
| Descrizione fisica | 1 online resource (281 p.) |
| Disciplina | 006.3 |
| Collana | Quantitative and network biology |
| Soggetto topico |
Electronic commerce
Computational intelligence |
| ISBN |
3-527-69154-5
3-527-69151-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Dedication; Contents; Color Plates; Preface; List of Contributors; Chapter 1 Model Selection for Neural Network Models: A Statistical Perspective; 1.1 Introduction; 1.2 Feedforward Neural Network Models; 1.3 Model Selection; 1.3.1 Feature Selection by Relevance Measures; 1.3.2 Some Numerical Examples; 1.3.3 Application to Real Data; 1.4 The Selection of the Hidden Layer Size; 1.4.1 A Reality Check Approach; 1.4.2 Numerical Examples by Using the Reality Check; 1.4.3 Testing Superior Predictive Ability for Neural Network Modeling
1.4.4 Some Numerical Results Using Test of Superior Predictive Ability1.4.5 An Application to Real Data; 1.5 Concluding Remarks; References; Chapter 2 Measuring Structural Correlations in Graphs; 2.1 Introduction; 2.1.1 Solutions for Measuring Structural Correlations; 2.2 Related Work; 2.3 Self Structural Correlation; 2.3.1 Problem Formulation; 2.3.2 The Measure; 2.3.2.1 Random Walk and Hitting Time; 2.3.2.2 Decayed Hitting Time; 2.3.3 Computing Decayed Hitting Time; 2.3.3.1 Iterative Approximation; 2.3.3.2 A Sampling Algorithm for h(vi,B); 2.3.3.3 Complexity; 2.3.4 Assessing SSC 2.3.4.1 Estimating ρ (Vq)2.3.4.2 Estimating the Significance of ρ (Vq); 2.3.5 Empirical Studies; 2.3.5.1 Datasets; 2.3.5.2 Performance of DHT Approximation; 2.3.5.3 Effectiveness on Synthetic Events; 2.3.5.4 SSC of Real Event; 2.3.5.5 Scalability of Sampling-alg; 2.3.6 Discussions; 2.4 Two-Event Structural Correlation; 2.4.1 Preliminaries and Problem Formulation; 2.4.2 Measuring TESC; 2.4.2.1 The Test; 2.4.2.2 Reference Nodes; 2.4.3 Reference Node Sampling; 2.4.3.1 Batch_BFS; 2.4.3.2 Importance Sampling; 2.4.3.3 Global Sampling in Whole Graph; 2.4.3.4 Complexity Analysis; 2.4.4 Experiments 2.4.4.1 Graph Datasets2.4.4.2 Event Simulation Methodology; 2.4.4.3 Performance Comparison; 2.4.4.4 Batch Importance Sampling; 2.4.4.5 Impact of Graph Density; 2.4.4.6 Efficiency and Scalability; 2.4.4.7 Real Events; 2.4.5 Discussions; 2.5 Conclusions; Acknowledgments; References; Chapter 3 Spectral Graph Theory and Structural Analysis of Complex Networks: An Introduction; 3.1 Introduction; 3.2 Graph Theory: Some Basic Concepts; 3.2.1 Connectivity in Graphs; 3.2.2 Subgraphs and Special Graphs; 3.3 Matrix Theory: Some Basic Concepts; 3.3.1 Trace and Determinant of a Matrix 3.3.2 Eigenvalues and Eigenvectors of a Matrix3.4 Graph Matrices; 3.4.1 Adjacency Matrix; 3.4.2 Incidence Matrix; 3.4.3 Degree Matrix and Diffusion Matrix; 3.4.4 Laplace Matrix; 3.4.5 Cut-Set Matrix; 3.4.6 Path Matrix; 3.5 Spectral Graph Theory: Some Basic Results; 3.5.1 Spectral Characterization of Graph Connectivity; 3.5.1.1 Spectral Theory and Walks; 3.5.2 Spectral Characteristics of some Special Graphs and Subgraphs; 3.5.2.1 Tree; 3.5.2.2 Bipartite Graph; 3.5.2.3 Complete Graph; 3.5.2.4 Regular Graph; 3.5.2.5 Line Graph; 3.5.3 Spectral Theory and Graph Colouring 3.5.4 Spectral Theory and Graph Drawing |
| Record Nr. | UNINA-9910815219603321 |
| Weinheim, Germany : , : Wiley-VCH Verlang GmbH & Co. KGaA, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
