Computational Network Theory: Theoretical Foundations and Applications Author
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Von Händler/Antiquariat, Syndikat Buchdienst, [4235284].
AUSFÜHRLICHERE BESCHREIBUNG: Diese umfassende Einführung in die rechnergestützte Netzwerktheorie als ein Zweig der Netzwerktheorie baut auf dem Grundsatz auf, dass solche Netzwerke als Werkzeuge zu verstehen sind, mit denen sich durch die Anwendung rechnergestützter Verfahren auf große Mengen an Netzwerkdaten Hypothesen ableiten und verifizieren lassen. Ein Team aus erfahrenden Herausgebern und renommierten Autoren aus der ganzen Welt präsentieren und erläutern eine Vielzahl von repräsentativen Methoden der rechnergestützten Netzwerktheorie, die sich aus der Graphentheorie, rechnergestützten und statistischen Verfahren ableiten. Dieses Referenzwerk überzeugt durch einen einheitlichen Aufbau und Stil und eignet sich auch für Kurse zu rechnergestützten Netzwerken. INHALT: Color Plates XV Preface XXXI List of Contributors XXXIII 1 Model Selection for Neural Network Models: A Statistical Perspective 1 Michele La Rocca and Cira Perna 1.1 Introduction 1 1.2 Feedforward Neural NetworkModels 2 1.3 Model Selection 4 1.3.1 Feature Selection by Relevance Measures 6 1.3.2 Some Numerical Examples 10 1.3.3 Application to Real Data 12 1.4 The Selection of the Hidden Layer Size 14 1.4.1 A Reality Check Approach 15 1.4.2 Numerical Examples by Using the Reality Check 16 1.4.3 Testing Superior Predictive Ability for Neural Network Modeling 19 1.4.4 Some Numerical Results Using Test of Superior Predictive Ability 21 1.4.5 An Application to Real Data 23 1.5 Concluding Remarks 26 References 26 2 Measuring Structural Correlations in Graphs 29 Ziyu Guan and Xifeng Yan 2.1 Introduction 29 2.1.1 Solutions for Measuring Structural Correlations 31 2.2 RelatedWork 32 2.3 Self Structural Correlation 34 2.3.1 Problem Formulation 34 2.3.2 The Measure 34 2.3.3 Computing Decayed Hitting Time 37 2.3.4 Assessing SSC 41 2.3.5 Empirical Studies 45 2.3.6 Discussions 51 2.4 Two-Event Structural Correlation 52 2.4.1 Preliminaries and Problem Formulation 52 2.4.2 Measuring TESC 53 2.4.3 Reference Node Sampling 56 2.4.4 Experiments 62 2.4.5 Discussions 70 2.5 Conclusions 72 Acknowledgments 72 References 72 3 Spectral Graph Theory and Structural Analysis of Complex Networks: An Introduction 75 Salissou Moutari and Ashraf Ahmed 3.1 Introduction 75 3.2 Graph Theory: Some Basic Concepts 76 3.2.1 Connectivity in Graphs 77 3.2.2 Subgraphs and Special Graphs 80 3.3 MatrixTheory: Some Basic Concepts 81 3.3.1 Trace and Determinant of a Matrix 81 3.3.2 Eigenvalues and Eigenvectors of a Matrix 82 3.4 Graph Matrices 83 3.4.1 Adjacency Matrix 84 3.4.2 Incidence Matrix 84 3.4.3 Degree Matrix and Diffusion Matrix 85 3.4.4 Laplace Matrix 85 3.4.5 Cut-Set Matrix 86 3.4.6 Path Matrix 86 3.5 Spectral Graph Theory: Some Basic Results 86 3.5.1 Spectral Characterization of Graph Connectivity 87 3.5.2 Spectral Characteristics of some Special Graphs and Subgraphs 89 3.5.3 SpectralTheory and Graph Colouring 91 3.5.4 SpectralTheory and Graph Drawing 91 3.6 Computational Challenges for Spectral Graph Analysis 91 3.6.1 Krylov Subspace Methods 91 3.6.2 Constrained Optimization Approach 94 3.7 Conclusion 94 References 95 4 Contagion in Interbank Networks 97 Grzegorz Ha&sup3aj and Christoffer Kok 4.1 Introduction 97 4.2 Research Context 99 4.3 Models 103 4.3.1 Simulated Networks 104 4.3.2 Systemic Probability Index 109 4.3.3 Endogenous Networks 110 4.4 Results 119 4.4.1 Data 119 4.4.2 Simulated Networks 120 4.4.3 Structure of Endogenous Interbank Networks 123 4.5 Stress Testing Applications 127 4.6 Conclusions 130 References 131 5 Detection, Localization, and Tracking of a Single and Multiple Targets with Wireless Sensor Networks 137 Natallia Katenka 5.1 Introduction and Overview 137 5.2 Data Collection and Fusion by WSN 138 5.3 Target Detection 141 5.3.1 Target Detection from Value Fusion (Energies) 142 5.3.2 Target Detection from Ordinary Decision Fusion 143 5.3.3 Target Detection from Local Vote Decision Fusion 144 5.4 Single Target Localization and Diagnostic 149 5.4.1 Localization and Diagnostic from Value Fusion (Energies) 150 5.4.2 Localization and Diagnostic from Ordinary Decision Fusion 151 5.4.3 Localization and Diagnostic from Local Vote Decision Fusion 152 5.4.4 Hybrid Maximum Likelihood Estimates 153 5.4.5 Properties of Maximum-Likelihood Estimates 154 5.5 Multiple Target Localization and Diagnostic 157 5.5.1 Multiple Target Localization from Energies 158 5.5.2 Multiple Target Localization from Binary Decisions 158 5.5.3 Multiple Target Localization from Corrected Decision BIOGRAFIEN: Dehmer, Matthias: Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his PhD in computer science from the Technical University of Darmstadt (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). His research interests are in bioinformatics, cancer analysis, chemical graph theory, systems biology, complex networks, complexity, statistics and information theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology and medicinal chemistry. Emmert-Streib, Frank: Frank Emmert-Streib studied physics at the University of Siegen (Germany) and received his Ph.D. in Theoretical Physics from the University of Bremen (Germany). He was a postdoctoral research associate at the Stowers Institute for Medical Research (Kansas City, USA) in the Department for Bioinformatics and a Senior Fellow at the University of Washington (Seattle, USA) in the Department of Biostatistics and the Department of Genome Sciences. Currently, he is Lecturer/Assistant Professor at the Queen's University Belfast at the Center for Cancer Research and Cell Biology (CCRCB) leading the Computational Biology and Machine Learning Lab. His research interests are in the field of computational biology, machine learning and biostatistics in the development and application of methods from statistics and machine learning for the analysis of high-throughput data from genomics and genetics experiments. Dehmer, Matthias: Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his PhD in computer science from the Technical University of Darmstadt (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). His research interests are in bioinformatics, cancer analysis, chemical graph theory, systems biology, complex networks, complexity, statistics and information theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology and medicinal chemistry. Emmert-Streib, Frank: Frank Emmert-Streib studied physics at the University of Siegen (Germany) and received his Ph.D. in Theoretical Physics from the University of Bremen (Germany). He was a postdoctoral research associate at the Stowers Institute for Medical Research (Kansas City, USA) in the Department for Bioinformatics and a Senior Fellow at the University of Washington (Seattle, USA) in the Department of Biostatistics and the Department of Genome Sciences. Currently, he is Lecturer/Assistant Professor at the Queen's University Belfast at the Center for Cancer Research and Cell Biology (CCRCB) leading the Computational Biology and Machine Learning Lab. His research interests are in the field of computational biology, machine learning and biostatistics in the development and application of methods from statistics and machine learning for the analysis of high-throughput data from genomics and genetics experiments. 2015, Buch, gebundene Ausgabe, Neuware, H: 251mm, B: 175mm, T: 17mm, 762g, 242, Internationaler Versand, Selbstabholung und Barzahlung, PayPal, offene Rechnung, Banküberweisung.

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This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data. The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks. gebundene Ausgabe, 07.10.2015.

Lieferung aus: Vereinigte Staaten von Amerika, Lagernd.
This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data.The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks.

Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Despatched same working day before 3pm.
This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data.The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks.

Lieferung aus: Deutschland, Versandkostenfrei.
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