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
Table des matières
Chapter 1: Introduction
Chapter 2: Mathematical Foundations
Chapter 3: Research Design
Chapter 4: Data Collection
Chapter 5: Data Management
Chapter 6: Multivariate Techniques Used in Network Analysis
Chapter 7: Visualization
Chapter 8: Local Node-Level Measures
Chapter 9: Centrality
Chapter 10: Group-level measures
Chapter 11: Subgroups and community detection
Chapter 12: Equivalence
Chapter 13: Analyzing Two-mode Data
Chapter 14: Introduction to Inferential Statistics for Complete Networks
Chapter 15: ERGMs and SAOMs
A propos de l’auteur
Filip Agneessens is an Associate Professor at the Department of Sociology and Social Research, University of Trento. He has published on a diversity of topics related to social networks, including measures of centrality, statistical models, ego-networks and social support, two-mode networks, negative ties, multilevel networks and issues related to data collection. He has also applied social network analysis to understand the antecedents and consequences of interactions among employees, and in particular within teams. Together with Martin Everett, he was a guest-editor for a special issue on “Advances in Two-mode Social Network Analysis” in the journal Social Networks, and together with Nick Harrigan and Joe Labianca he guest-edited a special issue on “Negative and Signed Tie Networks”. He has taught numerous introductory and advanced social network courses and workshops over the last 15 years.
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