LEADER 05598nam 2200697 450 001 9910788289203321 005 20230807210352.0 010 $a3-527-69154-5 010 $a3-527-69151-0 035 $a(CKB)2670000000614220 035 $a(EBL)2038681 035 $a(SSID)ssj0001561664 035 $a(PQKBManifestationID)16203883 035 $a(PQKBTitleCode)TC0001561664 035 $a(PQKBWorkID)14832889 035 $a(PQKB)11155773 035 $a(MiAaPQ)EBC2038681 035 $a(MiAaPQ)EBC4042601 035 $a(Au-PeEL)EBL4042601 035 $a(CaPaEBR)ebr11246244 035 $a(CaONFJC)MIL778978 035 $a(OCoLC)908335775 035 $a(EXLCZ)992670000000614220 100 $a20160827h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aComputational network theory $etheoretical foundations and applications /$fEdited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl 210 1$aWeinheim, Germany :$cWiley-VCH Verlang GmbH & Co. KGaA,$d2015. 210 4$dİ2015 215 $a1 online resource (281 p.) 225 1 $aQuantitative and network biology ;$vVolume 5 300 $aDescription based upon print version of record. 311 $a3-527-69153-7 311 $a3-527-33724-5 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCover; 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 327 $a1.4.4 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 327 $a2.3.4.1 Estimating ? (Vq)2.3.4.2 Estimating the Significance of ? (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 327 $a2.4.4.1 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 327 $a3.3.2 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 327 $a3.5.4 Spectral Theory and Graph Drawing 330 $aThis comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data.The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for 410 0$aQuantitative and network biology ;$vVolume 5. 606 $aElectronic commerce 606 $aComputational intelligence 615 0$aElectronic commerce. 615 0$aComputational intelligence. 676 $a006.3 702 $aPickl$b Stefan 702 $aEmmert-Streib$b Frank 702 $aDehmer$b Matthias 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910788289203321 996 $aComputational network theory$93855477 997 $aUNINA