4.A Networks Methods
📖 Books
Network Analysis: Integrating Social Network Theory, Method, and Application with R
Craig Rawlings, Jeffrey A. Smith, James Moody, and Daniel McFarland
“The size and availability of network information has exploded over the last decade. Social scientists now share the stage of network analysis with computer scientists, physicists, and statisticians. While a number of introductions to network analysis are now available, most focus on theory, methods, or application alone. This book integrates all three. Network Analysis is an introduction to both the why and how of Social Network Analysis (SNA). It presents a broad theoretical overview rooted in social scientific approaches and guides users in how network analysis can answer core theoretical questions. It provides a comprehensive overview of descriptive and analytical approaches, including practical tutorials in R with sample data sets. Using an integrated approach, this book aims to quickly bring novice network researchers up to speed while avoiding common programming and analysis mistakes so that they might gain insight into the fundamental theories, key concepts, and methodological application of SNA.”
submitted by Graham Ambrose
Statistical Analysis of Network Data with R
📖 Link: https://link.springer.com/book/10.1007/978-1-4939-0983-4#otherversion=9781493909827
Eric D. Kolaczyk and Gábor Csárdi
“TNetworks 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).”
🌐 Web Resources
Network Analysis: Integrating Social Network Theory, Method, and Application with R
🌐 Link: https://inarwhal.github.io/NetworkAnalysisR-book/
Craig Rawlings, Jeffrey A. Smith, James Moody, and Daniel McFarland
This is the website for Network Analysis: Integrating Social Network Theory, Method, and Application with R. Here you will find the R tutorials that accompany the printed manuscript, which is available through Cambridge University Press.
submitted by Graham Ambrose
Handbook of Graphs and Networks in People Analytics
🌐 Link: https://ona-book.org/
Keith McNulty
“This book aims to make graph and network analysis more approachable for students and professionals by explaining core theory and demonstrating common methodologies using open-source programming languages such as R and Python. It balances theoretical foundations with practical implementation through example datasets and code snippets that support analysis and interpretation. Readers will learn essential skills, from creating and visualizing graphs to applying measures such as graph density and centrality and algorithms for graph partitioning and community detection. These methods have broad applications, particularly in organizational settings, where they can support onboarding, foster diverse collaboration, improve communication, identify organizational structures aligned with workflows, detect collaborative groups, connect individuals with shared interests, and identify potential leaders.”
R for Social Network Analysis
🌐 Link: https://schochastics.github.io/R4SNA/
Termeh Shafie and David Schoch
“This is the website for “R for Social Network Analysis”, a practical guide to analyzing, visualizing, and modeling networks in R. The book walks through the standard descriptive toolkit, builds up
ggraph-based visualizations, and covers the main families of inferential models (ERGMs, stochastic actor-oriented models, and relational event models), before closing with a tidyverse-style take on network analysis viatidygraph.The book is aimed at researchers, students, and practitioners who already know a little R and want a guided tour of the network-analysis ecosystem without having to stitch together a dozen package vignettes themselves.”
Awesome Network Analysis
🌐 Link: https://github.com/briatte/awesome-network-analysis#books
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