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Analyzing Social Networks
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Analyzing Social Networks

Second Edition


February 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 new 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 1.7 Conclusion  
 
Chapter 2: Mathematical Foundations
Graphs  
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  
Normalization  
Cognitive social structure data  
Matching attributes and networks  
Converting attributes to matrices  
 
Chapter 6: Multivariate Techniques Used in Network Analysis
Multidimensional scaling  
Correspondence analysis  
Hierarchical clustering  
 
Chapter 7: Visualization
Layout  
Embedding node attributes  
Node filtering  
Ego networks  
Embedding tie characteristics  
Visualizing network change  
Exporting visualizations  
 
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
Cohesion  
Reciprocity  
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
Cliques  
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  
Blockmodels  
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  
Equivalence  
 
Chapter 14: Large Networks
Reducing the size of the problem  
Choosing appropriate methods  
Sampling  
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  

An excellent book for students and established scholars alike who want to seriously get into the analysis of social networks. The authors provide a superb introduction to the field, but also offer the depth that enables the reader to perform state-of-the-art analyses. Each chapter comes with clearly defined learning outcomes and exercises, which makes me recommend this book to all my students. It is one of the best books on the analysis of social networks that I have seen so far. 

Thomas Grund
Sociology, University College Dublin

The first edition of this fine text has quickly become a leading resource for the conduct of social network research and the analysis of social network data, especially for those researchers using the UCINET software to analyse data. So it is especially valuable to see an updated second edition appearing. This is an indispensable guide for researchers in the collection, analysis and interpretation of social network data. 

Garry Robins
Psychological Sciences, University of Melbourne

Other books are about social networks. Look here for the best introduction to doing network research. If you want to learn to design a network study, analyze networks, and test hypotheses about social connectivity, this is the book for you.

Ronald Breiger
Regents' Professor, University of Arizona

The first edition of this book was a winner … and this edition is even better. The clear writing, the new glossary at the end of the book, and the exercises at the end of each chapter make this edition a wonderful book to teach from.  Highly recommended. 

H. Russell Bernard
Director, Institute for Social Science Research, Arizona State University

What do rumours, viruses and global trade have in common? They are all transmitted through a network. For some, this is the start of thinking how all networks share similar properties. For me, such platitudes are getting passé; of course networks are everywhere! Finally, this book goes beyond superficial commonalities in networks to provide a coherent framework for the many different kinds of social networks available to the researcher. The authors help us understand which differences matter, how to analyse them and how to make sense of the results. These days its easy to be sold on the power of network analysis, but it is much harder to know which analysis to do and why. Thankfully, Borgatti, Everett and Johnson have given us a text that is as conceptually rich as it is methodologically generous. 

Bernie Hogan
Senior Research Fellow, Oxford Internet Institute, University of Oxford

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