06281nam 2200841 a 450 991014126380332120230912123728.01-280-87271-397866137140221-118-34698-X1-118-34699-81-118-34701-3(CKB)2670000000207560(EBL)894394(SSID)ssj0000679238(PQKBManifestationID)11469988(PQKBTitleCode)TC0000679238(PQKBWorkID)10609354(PQKB)10443429(DLC) 2012010295(Au-PeEL)EBL894394(CaPaEBR)ebr10575598(CaONFJC)MIL371402(Au-PeEL)EBL4034453(CaPaEBR)ebr11109800(PPN)170610969(OCoLC)779740472(FR-PaCSA)88813009(CaSebORM)9781118346983(MiAaPQ)EBC894394(EXLCZ)99267000000020756020120308d2012 uy 0engur|n|---|||||txtccrStatistical and machine learning approaches for network analysis /edited by Matthias Dehmer, Subhash C. Basak1st editionHoboken, N.J. Wiley20121 online resource (345 p.)Wiley series in computational statistics ;707Description based upon print version of record.0-470-19515-0 Includes bibliographical references and index.Statistical 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 Method1.4.1.6 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; REFERENCES2 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 Model2.7 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 INTRODUCTION3.2 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 Data3.4 BIPARTITE RELATIONSHIP: A CASE STUDY OF METABOLITE DISTRIBUTION"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"--Provided by publisher.Wiley Series in Computational StatisticsResearchStatistical methodsMachine theoryCommunicationNetwork analysisGraphic methodsInformation scienceStatistical methodsResearchStatistical methods.Machine theory.CommunicationNetwork analysisGraphic methods.Information scienceStatistical methods.511/.5MAT029000bisacshDehmer Matthias1968-860612Dehmer Matthias1968-860612Basak Subhash C.1945-911653MiAaPQMiAaPQMiAaPQBOOK9910141263803321Statistical and machine learning approaches for network analysis2041495UNINA