LEADER 04319nam 22006015 450 001 996418184603316 005 20240613194443.0 010 $a3-030-44129-6 024 7 $a10.1007/978-3-030-44129-6 035 $a(CKB)5280000000218692 035 $a(MiAaPQ)EBC6219823 035 $a(DE-He213)978-3-030-44129-6 035 $a(PPN)248595970 035 $a(EXLCZ)995280000000218692 100 $a20200602d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Analysis of Network Data with R /$fby Eric D. Kolaczyk, Gábor Csárdi 205 $a2nd ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (235 pages) 225 1 $aUse R!,$x2197-5736 311 $a3-030-44128-8 327 $a1 Introduction -- 2 Manipulating Network Data -- 3 Visualizing Network Data -- 4 Descriptive Analysis of Network Graph Characteristics -- 5 Mathematical Models for Network Graphs -- 6 Statistical Models for Network Graphs -- 7 Network Topology Inference -- 8 Modeling and Prediction for Processes on Network Graphs -- 9 Analysis of Network Flow Data -- 10 Networked Experiments -- 11 Dynamic Networks -- Index. 330 $aThe new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualization and characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well. 410 0$aUse R!,$x2197-5736 606 $aStatistics  606 $aComputer communication systems 606 $aElectrical engineering 606 $aR (Computer program language) 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 615 0$aStatistics . 615 0$aComputer communication systems. 615 0$aElectrical engineering. 615 0$aR (Computer program language). 615 14$aStatistics and Computing/Statistics Programs. 615 24$aComputer Communication Networks. 615 24$aCommunications Engineering, Networks. 676 $a003 700 $aKolaczyk$b Eric D$4aut$4http://id.loc.gov/vocabulary/relators/aut$0472339 702 $aCsárdi$b Gábor$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418184603316 996 $aStatistical Analysis of Network Data with R$92347961 997 $aUNISA