Stephen P. Borgatti, Martin G. Everett, Jeffrey C. Johnson, Filip Agneessens

Analyzing Social Networks Using R (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, D ausgeliefert werden.

Produktdetails

• Verlag: SAGE Publications
• Seitenzahl: 384
• Erscheinungstermin: 21. April 2022
• Englisch
• ISBN-13: 9781529765755
• Artikelnr.: 62865367

Autorenporträt

Stephen Borgatti is the Gatton Endowed Chair of Management at the Gatton College of Business and Economics at the University of Kentucky. He has published extensively in management journals, as well cross-disciplinary journals such as Science and Social Networks. He has published over 100 peer-reviewed articles on network analysis, garnering more than 70,000 Google Scholar citations. With Martin Everett, Steve is co-author of UCINET, a well-known software package for social network analysis, as well as founder of the annual LINKS Center workshop on social network analysis. He is also a 2-term past President of INSNA (the professional association for network researchers) and winner of their Simmel Award for lifetime achievement.

Inhaltsangabe

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