LEADER 06281nam 2200841 a 450 001 9910141263803321 005 20230912123728.0 010 $a1-280-87271-3 010 $a9786613714022 010 $a1-118-34698-X 010 $a1-118-34699-8 010 $a1-118-34701-3 035 $a(CKB)2670000000207560 035 $a(EBL)894394 035 $a(SSID)ssj0000679238 035 $a(PQKBManifestationID)11469988 035 $a(PQKBTitleCode)TC0000679238 035 $a(PQKBWorkID)10609354 035 $a(PQKB)10443429 035 $a(DLC) 2012010295 035 $a(Au-PeEL)EBL894394 035 $a(CaPaEBR)ebr10575598 035 $a(CaONFJC)MIL371402 035 $a(Au-PeEL)EBL4034453 035 $a(CaPaEBR)ebr11109800 035 $a(PPN)170610969 035 $a(OCoLC)779740472 035 $a(FR-PaCSA)88813009 035 $a(CaSebORM)9781118346983 035 $a(MiAaPQ)EBC894394 035 $a(EXLCZ)992670000000207560 100 $a20120308d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aStatistical and machine learning approaches for network analysis /$fedited by Matthias Dehmer, Subhash C. Basak 205 $a1st edition 210 $aHoboken, N.J. $cWiley$d2012 215 $a1 online resource (345 p.) 225 0 $aWiley series in computational statistics ;$v707 300 $aDescription based upon print version of record. 311 $a0-470-19515-0 320 $aIncludes bibliographical references and index. 327 $aStatistical and Machine Learning Approaches for Network Analysis; Contents; Preface; Contributors; 1 A Survey of Computational Approaches to Reconstruct and Partition Biological Networks; 1.1 INTRODUCTION; 1.2 BIOLOGICAL NETWORKS; 1.2.1 Directed Networks; 1.2.2 Undirected Networks; 1.3 GENOME-WIDE MEASUREMENTS; 1.3.1 Gene Expression Data; 1.3.2 Gene Sets; 1.4 RECONSTRUCTION OF BIOLOGICAL NETWORKS; 1.4.1 Reconstruction of Directed Networks; 1.4.1.1 Boolean Networks; 1.4.1.2 Probabilistic Boolean Networks; 1.4.1.3 Bayesian Networks; 1.4.1.4 Collaborative Graph Model; 1.4.1.5 Frequency Method 327 $a1.4.1.6 EM-Based Inference from Gene Sets1.4.2 Reconstruction of Undirected Networks; 1.4.2.1 Relevance Networks; 1.4.2.2 Graphical Gaussian Models; 1.5 PARTITIONING BIOLOGICAL NETWORKS; 1.5.1 Directed and Undirected Networks; 1.5.2 Partitioning Undirected Networks; 1.5.2.1 Kernighan-Lin Algorithm; 1.5.2.2 Girvan-Newman Algorithm; 1.5.2.3 Newman's Eigenvector Method; 1.5.2.4 Infomap; 1.5.2.5 Clique Percolation Method; 1.5.3 Partitioning Directed Networks; 1.5.3.1 Newman's Eigenvector Method; 1.5.3.2 Infomap; 1.5.3.3 Clique Percolation Method; 1.6 DISCUSSION; REFERENCES 327 $a2 Introduction to Complex Networks: Measures, Statistical Properties, and Models2.1 INTRODUCTION; 2.2 REPRESENTATION OF NETWORKS; 2.3 CLASSICAL NETWORK; 2.3.1 Random Network; 2.3.2 Lattice Network; 2.4 SCALE-FREE NETWORK; 2.4.1 Degree Distribution; 2.4.2 Degree Distribution of Random Network; 2.4.3 Power-Law Distribution in Real-World Networks; 2.4.4 Barab ?asi-Albert Model; 2.4.5 Configuration Model; 2.5 SMALL-WORLD NETWORK; 2.5.1 Average Shortest Path Length; 2.5.2 Ultrasmall-World Network; 2.6 CLUSTERED NETWORK; 2.6.1 Clustering Coefficient; 2.6.2 Watts-Strogatz Model 327 $a2.7 HIERARCHICAL MODULARITY2.7.1 Hierarchical Model; 2.7.2 Dorogovtsev-Mendes-Samukhin Model; 2.8 NETWORK MOTIF; 2.9 ASSORTATIVITY; 2.9.1 Assortative Coefficient; 2.9.2 Degree Correlation; 2.9.3 Linear Preferential Attachment Model; 2.9.4 Edge Rewiring Method; 2.10 RECIPROCITY; 2.11 WEIGHTED NETWORKS; 2.11.1 Strength; 2.11.2 Weighted Clustering Coefficient; 2.11.3 Weighted Degree Correlation; 2.12 NETWORK COMPLEXITY; 2.13 CENTRALITY; 2.13.1 Definition; 2.13.2 Comparison of Centrality Measures; 2.14 CONCLUSION; REFERENCES; 3 Modeling for Evolving Biological Networks; 3.1 INTRODUCTION 327 $a3.2 UNIFIED EVOLVING NETWORK MODEL: REPRODUCTION OF HETEROGENEOUS CONNECTIVITY, HIERARCHICAL MODULARITY, AND DISASSORTATIVITY3.2.1 Network Model; 3.2.2 Degree Distribution; 3.2.3 Degree-Dependent Clustering Coefficient; 3.2.4 Average Clustering Coefficient; 3.2.5 Degree Correlation; 3.2.6 Assortative Coefficient; 3.2.7 Comparison with Real Data; 3.3 MODELING WITHOUT PARAMETER TUNING: A CASE STUDY OF METABOLIC NETWORKS; 3.3.1 Network Model; 3.3.2 Analytical Solution; 3.3.3 Estimation of the Parameters; 3.3.4 Comparison with Real Data 327 $a3.4 BIPARTITE RELATIONSHIP: A CASE STUDY OF METABOLITE DISTRIBUTION 330 $a"This book explores novel graph classes and presents novel methods to classify networks. It particularly addresses the following problems: exploration of novel graph classes and their relationships among each other; existing and classical methods to analyze networks; novel graph similarity and graph classification techniques based on machine learning methods; and applications of graph classification and graph mining. Key topics are addressed in depth including the mathematical definition of novel graph classes, i.e. generalized trees and directed universal hierarchical graphs, and the application areas in which to apply graph classes to practical problems in computational biology, computer science, mathematics, mathematical psychology, etc"--$cProvided by publisher. 410 0$aWiley Series in Computational Statistics 606 $aResearch$xStatistical methods 606 $aMachine theory 606 $aCommunication$xNetwork analysis$xGraphic methods 606 $aInformation science$xStatistical methods 615 0$aResearch$xStatistical methods. 615 0$aMachine theory. 615 0$aCommunication$xNetwork analysis$xGraphic methods. 615 0$aInformation science$xStatistical methods. 676 $a511/.5 686 $aMAT029000$2bisacsh 700 $aDehmer$b Matthias$f1968-$0860612 701 $aDehmer$b Matthias$f1968-$0860612 701 $aBasak$b Subhash C.$f1945-$0911653 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141263803321 996 $aStatistical and machine learning approaches for network analysis$92041495 997 $aUNINA