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Analyzing Social Networks
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Analyzing Social Networks

Second Edition
Additional resources:


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 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
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

 
Data export

 
 
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

 
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
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

Probably the best method-focused book on social network analysis I have ever read. Clear structure, precise language, great examples. Can not recommend this enough for researchers and students who are interested in learning about social network analysis.
I would not recommend it for bachelor level students as it seems too in-depth to be pure introductory material.

Mr Steffen Triebel
Institut of Management and Or, University of Hannover
January 15, 2019

It gives you just the right amount of info
Good book to have around if you are interested in social network analysis

Miss burcu gumus
Communication Sciences, Dogus University
September 27, 2018

a very useful read/text

Mr Phillip Morgan
Faculty of Education and Training, University of Wales, Trinity St David
January 11, 2018
Key features
NEW AND KEY FEATURES:
  • Written by the creators of the software discussed in the book.
  • Walks students through from basic concepts to complex applications.
  • Fully updated digital resources including examples, walkthroughs, and exercises.

Sample Materials & Chapters

Introduction


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