Computational network theory : theoretical foundations and applications /

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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-pro...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Dehmer, Matthias, 1968- (Editor ), Emmert-Streib, Frank (Editor ), Pickl, Stefan, 1967- (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Weinheim : Wiley-VCH Verlang GmbH & Co. KGaA, [2015]
Colección:Quantitative and network biology ; v. 5.
Temas:
Computational intelligence.
Intelligence informatique.
COMPUTERS > Intelligence & Semantics.
Computational intelligence
Acceso en línea:Texto completo

MARC

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245 0 0 |a Computational network theory :  |b theoretical foundations and applications /  |c edited by Matthias Dehmer, Frank Emmert-Streib and Stefan Pickl. 
264 1 |a Weinheim :  |b Wiley-VCH Verlang GmbH & Co. KGaA,  |c [2015] 
300 |a 1 online resource :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Quantitative and network biology series ;  |v volume 5 
588 0 |a Online resource; title from PDF title page (Ebsco, viewed May 11, 2015). 
504 |a Includes bibliographical references and index. 
520 |a 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. 
505 0 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Computational intelligence. 
650 6 |a Intelligence informatique. 
650 7 |a COMPUTERS  |x Intelligence & Semantics.  |2 bisacsh 
650 7 |a Computational intelligence  |2 fast 
700 1 |a Dehmer, Matthias,  |d 1968-  |e editor.  |1 https://id.oclc.org/worldcat/entity/E39PCjvYRwH37jVKJFRxgp7T73 
700 1 |a Emmert-Streib, Frank,  |e editor. 
700 1 |a Pickl, Stefan,  |d 1967-  |e editor.  |1 https://id.oclc.org/worldcat/entity/E39PCjFMPcTXVkvDTRJTXgfjG3 
758 |i has work:  |a Computational network theory (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCFVJ7HD36vpqjDdjhFwWtC  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Dehmer, Matthias.  |t Computational Network Theory : Theoretical Foundations and Applications.  |d Hoboken : Wiley, ©2015  |z 9783527337248 
830 0 |a Quantitative and network biology ;  |v v. 5. 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4042601  |z Texto completo 
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