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Analyzing Social Networks

Analyzing Social Networks

Second Edition

January 2018 | 384 pages | SAGE Publications Ltd

Designed to walk beginners through core aspects of collecting, visualizing, analyzing, and interpreting social network data, this book will get you up-to-speed on the theory and skills you need to conduct social network analysis. Using simple language and equations, the authors provide expert, clear insight into every step of the research process—including basic maths principles—without making assumptions about what you know. With a particular focus on NetDraw and UCINET, the book introduces relevant software tools step-by-step in an easy to follow way.

In addition to the fundamentals of network analysis and the research process, this Second Edition focuses on:

  • Digital data and social networks like Twitter
  • Statistical models to use in SNA, like QAP and ERGM
  • The structure and centrality of networks
  • Methods for cohesive subgroups/community detection

Supported by new chapter exercises, a glossary, and a fully updated companion website, this text is the perfect student-friendly introduction to social network analysis. 

Chapter 1: Introduction
Why networks?  
What are networks?  
Types of relations  
Goals of analysis  
Network variables as explanatory variables  
Network variables as outcome variables  
Chapter 2: Mathematical Foundations
Paths and components  
Adjacency matrices  
Ways and modes  
Matrix products  
Chapter 3: Research Design
Experiments and field studies  
Whole-network and personal-network research designs  
Sources of network data  
Types of nodes and types of ties  
Actor attributes  
Sampling and bounding  
Sources of data reliability and validity issues  
Ethical considerations  
Chapter 4: Data Collection
Network questions  
Question formats  
Interviewee burden  
Data collection and reliability  
Archival data collection  
Data from electronic sources  
Chapter 5: Data Management
Data import  
Cleaning network data  
Data transformation  
Cognitive social structure data  
Matching attributes and networks  
Converting attributes to matrices  
Data export  
Chapter 6: Multivariate Techniques Used in Network Analysis
Multidimensional scaling  
Correspondence analysis  
Hierarchical clustering  
Chapter 7: Visualization
Embedding node attributes  
Node filtering  
Ego networks  
Embedding tie characteristics  
Visualizing network change  
Exporting visualizations  
Closing comments  
Chapter 8: Testing Hypotheses
Permutation tests  
Dyadic hypotheses  
Mixed dyadic–monadic hypotheses  
Node level hypotheses  
Whole-network hypotheses  
Exponential random graph models  
Stochastic actor-oriented models (SAOMs)  
Chapter 9: Characterizing Whole Networks
Transitivity and the clustering coefficient  
Triad census  
Centralization and core–periphery indices  
Chapter 10: Centrality
Basic concept  
Undirected, non-valued networks  
Directed, non-valued networks  
Valued networks  
Negative tie networks  
Chapter 11: Subgroups
Girvan–Newman algorithm  
Factions and modularity optimization  
Directed and valued data  
Computational considerations  
Performing a cohesive subgraph analysis  
Supplementary material  
Chapter 12: Equivalence
Structural equivalence  
Profile similarity  
The direct method  
Regular equivalence  
The REGE algorithm  
Core–periphery models  
Chapter 13: Analyzing Two-mode Data
Converting to one-mode data  
Converting valued two-mode matrices to one-mode  
Bipartite networks  
Cohesive subgroups and community detection  
Core–periphery models  
Chapter 14: Large Networks
Reducing the size of the problem  
Choosing appropriate methods  
Small-world and scale-free networks  
Chapter 15: Ego Networks
Personal-network data collection  
Analyzing ego network data  
Example 1 of an ego network study  
Example 2 of an ego network study  

Sample Materials & Chapters


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ISBN: 9781526404107

ISBN: 9781526404091