Analyzing Social Networks Using R

This approachable book introduces network research in R, walking you through every step of doing social network analysis.

Informationen zum Autor 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. Klappentext This approachable book introduces network research in R, walking you through every step of doing social network analysis. Zusammenfassung This approachable book introduces network research in R, walking you through every step of doing social network analysis. Inhaltsverzeichnis 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

Inhalt

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