| Description |
1 online resource : illustrations (some color) |
| Series |
Quantitative and network biology series ; volume 5 |
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Quantitative and network biology ; v. 5.
|
| Contents |
Titles of the Series "Quantitative and Network Biology"; Related Titles; Title Page; Copyright; Table of Contents; Dedication; 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; 1.4 The Selection of the Hidden Layer Size; 1.5 Concluding Remarks; References; Chapter 2: Measuring Structural Correlations in Graphs; 2.1 Introduction; 2.2 Related Work; 2.3 Self Structural Correlation; 2.4 Two-Event Structural Correlation; 2.5 Conclusions; References |
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Chapter 3: Spectral Graph Theory and Structural Analysis of Complex Networks: An Introduction3.1 Introduction; 3.2 Graph Theory: Some Basic Concepts; 3.3 Matrix Theory: Some Basic Concepts; 3.4 Graph Matrices; 3.5 Spectral Graph Theory: Some Basic Results; 3.6 Computational Challenges for Spectral Graph Analysis; 3.7 Conclusion; References; Chapter 4: Contagion in Interbank Networks; 4.1 Introduction; 4.2 Research Context; 4.3 Models; 4.4 Results; 4.5 Stress Testing Applications; 4.6 Conclusions; References |
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Chapter 5: Detection, Localization, and Tracking of a Single and Multiple Targets with Wireless Sensor Networks5.1 Introduction and Overview; 5.2 Data Collection and Fusion by WSN; 5.3 Target Detection; 5.4 Single Target Localization and Diagnostic; 5.5 Multiple Target Localization and Diagnostic; 5.6 Multiple Target Tracking; 5.7 Applications and Case Studies; 5.8 Final Remarks; References; Chapter 6: Computing in Dynamic Networks; 6.1 Introduction; 6.2 Preliminaries; 6.3 Spread of Influence in Dynamic Graphs (Causal Influence); 6.4 Naming and Counting in Anonymous Unknown Dynamic Networks |
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6.5 Causality, Influence, and Computation in Possibly Disconnected Synchronous Dynamic Networks6.6 Local Communication Windows; 6.7 Conclusions; References; Chapter 7: Visualization and Interactive Analysis for Complex Networks by means of Lossless Network Compression; 7.1 Introduction; 7.2 Power Graph Algorithm; 7.3 Validation-Edge Reduction Differs from Random; 7.4 Graph Comparison with Power Graphs; 7.5 Excursus: Layout of Power Graphs; 7.6 Interactive Visual Analytics; 7.7 Conclusion; References; Index; End User License Agreement |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (Ebsco, viewed May 11, 2015) |
| Subject |
Computational intelligence.
|
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COMPUTERS -- Intelligence & Semantics.
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Computational intelligence
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| Form |
Electronic book
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| Author |
Dehmer, Matthias, 1968- editor.
|
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Emmert-Streib, Frank, editor.
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Pickl, Stefan, 1967- editor.
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| ISBN |
9783527691531 |
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3527691537 |
|
9783527691548 |
|
3527691545 |
|
9783527691524 |
|
3527691529 |
|
9783527691517 |
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3527691510 |
|
