Computational Network Analysis With R

Author : Matthias Dehmer,Yongtang Shi,Frank Emmert-Streib
Publisher : John Wiley & Sons
Page : 364 pages
File Size : 41,5 Mb
Release : 2016-12-12
Category : Medical
ISBN : 9783527339587

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Computational Network Analysis with R by Matthias Dehmer,Yongtang Shi,Frank Emmert-Streib Pdf

This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

Author : Alboukadel Kassambara
Publisher : STHDA
Page : 39 pages
File Size : 53,8 Mb
Release : 2017-11-26
Category : Electronic
ISBN : 9781981179671

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Network Analysis and Visualization in R by Alboukadel Kassambara Pdf

Social network analysis is used to investigate the inter-relationship between entities. Examples of network structures, include: social media networks, friendship networks and collaboration networks. This book provides a quick start guide to network analysis and visualization in R. You'll learn, how to: - Create static and interactive network graphs using modern R packages. - Change the layout of network graphs. - Detect important or central entities in a network graph. - Detect community (or cluster) in a network.

Author : Gençer, Mehmet
Publisher : IGI Global
Page : 284 pages
File Size : 55,6 Mb
Release : 2020-02-07
Category : Computers
ISBN : 9781799819141

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Applied Social Network Analysis With R: Emerging Research and Opportunities by Gençer, Mehmet Pdf

Understanding the social relations within the fields of business and economics is vital for the promotion of success within a certain organization. Analytics and statistics have taken a prominent role in marketing and management practices as professionals are constantly searching for a competitive advantage. Converging these technological tools with traditional methods of business relations is a trending area of research. Applied Social Network Analysis With R: Emerging Research and Opportunities is an essential reference source that materializes and analyzes the issue of structure in terms of its effects on human societies and the state of the individuals in these communities. Even though the theme of the book is business-oriented, an approach underlining and strengthening the ties of this field of study with social sciences for further development is adopted throughout. Therefore, the knowledge presented is valid for analyzing not only the organization of the business world but also for the organization of any given community. Featuring research on topics such as network visualization, graph theory, and micro-dynamics, this book is ideally designed for researchers, practitioners, business professionals, managers, programmers, academicians, and students seeking coverage on analyzing social and business networks using modern methods of statistics, programming, and data sets.

Author : Matthias Dehmer,Yongtang Shi,Frank Emmert-Streib
Publisher : John Wiley & Sons
Page : 368 pages
File Size : 45,6 Mb
Release : 2016-07-22
Category : Medical
ISBN : 9783527694402

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Computational Network Analysis with R by Matthias Dehmer,Yongtang Shi,Frank Emmert-Streib Pdf

This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

Author : Michal Bojanowski
Publisher : John Wiley & Sons
Page : 0 pages
File Size : 40,5 Mb
Release : 2022-04-22
Category : Electronic
ISBN : 1118456041

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Introduction to Social Network Analysis with R by Michal Bojanowski Pdf

Introduction to Social Network Analysis with R provides an introduction to performing SNA studies using R, combining the theories of social networks and methods of social network analysis with the R environment as an open source system for statistical data analysis and graphics.

Author : Eric D. Kolaczyk,Gábor Csárdi
Publisher : Springer
Page : 214 pages
File Size : 40,5 Mb
Release : 2014-05-22
Category : Computers
ISBN : 9781493909834

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Statistical Analysis of Network Data with R by Eric D. Kolaczyk,Gábor Csárdi Pdf

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).

Author : Douglas Luke
Publisher : Springer
Page : 238 pages
File Size : 44,6 Mb
Release : 2015-12-14
Category : Mathematics
ISBN : 9783319238838

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A User’s Guide to Network Analysis in R by Douglas Luke Pdf

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.

Author : Eric D. Kolaczyk,Gabor Csardi
Publisher : Unknown
Page : 224 pages
File Size : 51,6 Mb
Release : 2014-06-30
Category : Electronic
ISBN : 1493909843

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Statistical Analysis of Network Data with R by Eric D. Kolaczyk,Gabor Csardi Pdf

Author : Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert
Publisher : CRC Press
Page : 500 pages
File Size : 52,5 Mb
Release : 2021-09-15
Category : Mathematics
ISBN : 9781000435634

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Doing Meta-Analysis with R by Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert Pdf

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book

Author : Stephen P. Borgatti,Martin G. Everett,Jeffrey C. Johnson,Filip Agneessens
Publisher : SAGE
Page : 332 pages
File Size : 54,6 Mb
Release : 2022-04-21
Category : Social Science
ISBN : 9781529765755

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Analyzing Social Networks Using R by Stephen P. Borgatti,Martin G. Everett,Jeffrey C. Johnson,Filip Agneessens Pdf

This approachable book introduces network research in R, walking you through every step of doing social network analysis. Drawing together research design, data collection and data analysis, it explains the core concepts of network analysis in a non-technical way. The book balances an easy to follow explanation of the theoretical and statistical foundations underpinning network analysis with practical guidance on key steps like data management, preparation and visualisation. With clarity and expert insight, it: • Discusses measures and techniques for analyzing social network data, including digital media • Explains a range of statistical models including QAP and ERGM, giving you the tools to approach different types of networks • Offers digital resources like practice datasets and worked examples that help you get to grips with R software

Author : Douglas Luke
Publisher : Unknown
Page : 128 pages
File Size : 50,8 Mb
Release : 2015
Category : Electronic
ISBN : 3319238841

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A User's Guide to Network Analysis in R by Douglas Luke Pdf

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.

Author : Keith McNulty
Publisher : CRC Press
Page : 269 pages
File Size : 47,5 Mb
Release : 2022-06-19
Category : Business & Economics
ISBN : 9781000597233

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Handbook of Graphs and Networks in People Analytics by Keith McNulty Pdf

Immediately implementable code, with extensive and varied illustrations of graph variants and layouts. Examples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigation. Dedicated chapter on graph visualization methods. Practical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachment. Various downloadable data sets for use both in class and individual learning projects. Final chapter dedicated to individual or group project examples.

Author : Bogumil Kaminski,Pawel Prałat,Francois Theberge
Publisher : CRC Press
Page : 278 pages
File Size : 54,7 Mb
Release : 2021-12-15
Category : Mathematics
ISBN : 9781000515855

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Mining Complex Networks by Bogumil Kaminski,Pawel Prałat,Francois Theberge Pdf

This book concentrates on mining networks, a subfield within data science. Data science uses scientific and computational tools to extract valuable knowledge from large data sets. Once data is processed and cleaned, it is analyzed and presented to support decision-making processes. Data science and machine learning tools have become widely used in companies of all sizes. Networks are often large-scale, decentralized, and evolve dynamically over time. Mining complex networks aim to understand the principles governing the organization and the behavior of such networks is crucial for a broad range of fields of study. Here are a few selected typical applications of mining networks: Community detection (which users on some social media platforms are close friends). Link prediction (who is likely to connect to whom on such platforms). Node attribute prediction (what advertisement should be shown to a given user of a particular platform to match their interests). Influential node detection (which social media users would be the best ambassadors of a specific product). This textbook is suitable for an upper-year undergraduate course or a graduate course in programs such as data science, mathematics, computer science, business, engineering, physics, statistics, and social science. This book can be successfully used by all enthusiasts of data science at various levels of sophistication to expand their knowledge or consider changing their career path. Jupiter notebooks (in Python and Julia) accompany the book and can be accessed on https://www.ryerson.ca/mining-complex-networks/. These not only contain all the experiments presented in the book, but also include additional material. Bogumił Kamiński is the Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He is also an Adjunct Professor at the Data Science Laboratory at Ryerson University. Bogumił is an expert in applications of mathematical modeling to solving complex real-life problems. He is also a substantial open-source contributor to the development of the Julia language and its package ecosystem. Paweł Prałat is a Professor of Mathematics in Ryerson University, whose main research interests are in random graph theory, especially in modeling and mining complex networks. He is the Director of Fields-CQAM Lab on Computational Methods in Industrial Mathematics in The Fields Institute for Research in Mathematical Sciences and has pursued collaborations with various industry partners as well as the Government of Canada. He has written over 170 papers and three books with 130 plus collaborators. François Théberge holds a B.Sc. degree in applied mathematics from the University of Ottawa, a M.Sc. in telecommunications from INRS and a PhD in electrical engineering from McGill University. He has been employed by the Government of Canada since 1996 where he was involved in the creation of the data science team as well as the research group now known as the Tutte Institute for Mathematics and Computing. He also holds an adjunct professorial position in the Department of Mathematics and Statistics at the University of Ottawa. His current interests include relational-data mining and deep learning.

Author : Craig M. Rawlings,Jeffrey A. Smith,Daniel A. McFarland,James Moody
Publisher : Cambridge University Press
Page : 477 pages
File Size : 46,5 Mb
Release : 2023-09-30
Category : Social Science
ISBN : 9781107037786

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Network Analysis by Craig M. Rawlings,Jeffrey A. Smith,Daniel A. McFarland,James Moody Pdf

A comprehensive yet accessible introduction to the theory, methods, and application of social network analysis.

Author : Matthias Dehmer
Publisher : Springer Science & Business Media
Page : 493 pages
File Size : 52,9 Mb
Release : 2010-10-14
Category : Mathematics
ISBN : 9780817647896

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Structural Analysis of Complex Networks by Matthias Dehmer Pdf

Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science, machine learning, artificial intelligence, computational and systems biology, cognitive science, computational linguistics, and mathematical chemistry. It may also be used as a supplementary textbook in graduate-level seminars on structural graph analysis, complex networks, or network-based machine learning methods.