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Titlebook: A User’s Guide to Network Analysis in R; Douglas Luke Book 2015 Springer International Publishing Switzerland 2015 R software.mathematical

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發(fā)表于 2025-3-21 17:20:02 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱A User’s Guide to Network Analysis in R
影響因子2023Douglas Luke
視頻videohttp://file.papertrans.cn/143/142557/142557.mp4
發(fā)行地址Provides a practical, hands-on tour of the major network analytic tasks R is capable of, including network data management, network visualization, network description, and network modeling.Includes al
學(xué)科分類Use R!
圖書封面Titlebook: A User’s Guide to Network Analysis in R;  Douglas Luke Book 2015 Springer International Publishing Switzerland 2015 R software.mathematical
影響因子Presenting a comprehensive resource for the mastery of network analysis in R,?the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and?readers are guided?through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will?describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.
Pindex Book 2015
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Clinical Trials — Phases I and IIswer, the state of the network data that you have available, and the intended audience(s) for the results of this work. The good news about performing network analysis in R is that, as will be seen in subsequent chapters, R provides a multitude of available network analysis options. However, it can
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Clinical Trials — Phases I and IIgraphic (as with any information graphic) is to highlight the important information contained in the underlying data. However, there are innumerable ways to visually layout network nodes and ties in two-dimensional space, as well as using graphical elements (e.g., node size, line color, figure legen
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V. Boonpucknavig,T. Srichaikul,S. Punyaguptacs. However, these plotting functions cannot meet all of the analytic or presentation needs. In particular, network scientists may wish to produce more specialized network graphics. Also, while . and . excel at producing high-quality publication ready network graphics, these graphics are static. For
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David G. I. Kingston,Maria Belen Cassera, networks affect their members based on where those members are located in the networks. A person who is connected to many other members of a network is likely to view the rest of the network quite differently from somebody who is relatively isolated from the other members.
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