Ebook Free Statistical Analysis of Network Data with R (Use R!), by Eric D. Kolaczyk, Gábor Csárdi
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Statistical Analysis of Network Data with R (Use R!), by Eric D. Kolaczyk, Gábor Csárdi
Ebook Free Statistical Analysis of Network Data with R (Use R!), by Eric D. Kolaczyk, Gábor Csárdi
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Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It 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. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).
- Sales Rank: #360739 in Books
- Published on: 2014-05-23
- Released on: 2014-05-23
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .53" w x 6.10" l, .0 pounds
- Binding: Paperback
- 207 pages
Review
“If students mastered this material, they would be well positioned to begin working on data and making further progress on their own. … SANDR covers a lot of basic and important material while teaching the reader how to work with data and models in R. … The book appears to be the only one available that covers the material at an introductory and practical level. … On the whole, I am happy to recommend it.” (Earl C. Lawrence, Journal of the American Statistical Association, June, 2015)
“This book presents contemporary mathematical and statistical methods of networks analysis and their implementation in R, written by the experts in this field … . The monograph presents an excellent description of a wide span of operations possible on networks, and is very useful for researchers and students.” (Stan Lipovetsky, Technometrics, Vol. 57 (2), May, 2015)
“This book is a quite practical guide to get started with analyzing networks using the statistical software R. … Relevant references are conveniently provided at the end of each chapter. … it is a very nice hands-on introduction to the analysis of network data that gives a good overview suitable for applied scientists and statisticians.” (Klaus Nordhausen, International Statistical Review, Vol. 83 (1), 2015)
From the Back Cover
Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It 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. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).
About the Author
Eric D. Kolaczyk�is a professor of statistics, and Director of the Program in Statistics, in the Department of Mathematics and Statistics at Boston University, where he also is an affiliated faculty member in the�Bioinformatics Program,� the Division of Systems�Engineering, and the Program in Computational Neuroscience. His publications on network-based topics, beyond the development of statistical methodology and theory, include�work on applications�ranging from the detection of anomalous traffic patterns in computer networks to the prediction of biological function in networks of interacting proteins�to the characterization of influence of groups of actors in social networks. He is an elected fellow of the American Statistical Association (ASA) and an elected senior member of the Institute of Electrical and Electronics Engineers (IEEE).�
G�bor Cs�rdi is a research associate at the Department of Statistics at Harvard University, Cambridge, Mass. He holds a PhD in Computer Science from E�tv�s University, Hungary. His research includes applications of network analysis in biology and social sciences, bioinformatics and computational biology, and graph algorithms. He created the igraph software package in 2005 and has been one of the lead developers since then.
Most helpful customer reviews
5 of 5 people found the following review helpful.
Good introduction to igraph
By cwats
This is a great introduction to the 'igraph' package for R. Someone who is just a beginner in using R can probably use this book, but it's probably best to have already been using it for some time. The 'igraph' package is pretty extensive, but this book will give you enough tools to explore what else the package can do.
It also covers the basics of doing network analyses. It doesn't go very deeply into any topics, really (nor does it have proofs or much theory), so it is best used in conjunction with (or perhaps after reading) Kolaczyk's other text (http://amzn.com/038788145X ). I used this after having used the other text.
2 of 2 people found the following review helpful.
Great book for network analysis (although you need to know ...
By letypka
Great book for network analysis (although you need to know a little bit of R before you can use it).
3 of 5 people found the following review helpful.
Good book
By Daniel Gibbons
Very good and concise introduction to the main statistical concepts you'll need to analyze networks and examples well integrated throughout.
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