Analyzing Social Networks

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. 

Table of Contents:
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

About the Author :
Stephen P. Borgatti, PhD 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 as cross-disciplinary journals such as Science and Social Networks. He has published over 100 peer-reviewed articles on network analysis, garnering more than 80,000 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 two-term past president of INSNA (the professional association for network researchers) and winner of their Simmel Award for lifetime achievement. Martin Everett is Professor of Social Network Analysis and co-director of the Mitchell Centre for SNA at the University of Manchester. He has published extensively on social network analysis and has over 100 peer-reviewed articles and consulted with government agencies as well as public and private companies. With Stephen Borgatti, Martin is co-author of UCINET, a well-known software package for social network analysis and is co-editor of the journal Social Networks. He is also a past President of INSNA (the professional association for network researchers) and winner of their Simmel Award for lifetime achievement. He was elected as an academician to the UK Academy of Social Sciences in 2004. Jeffrey C. Johnson, PhD is University Term Professor of Anthropology at the University of Florida. He is a former program manager with the Army Research Office (IPA), where he started the basic science research program in the social sciences. He has conducted extensive long-term research, supported by the National Science Foundation, comparing group dynamics and the evolution of social networks of over-wintering crews at the American South Pole Station with those at the Polish, Russian, Chinese, and Indian Antarctic Stations. In related research, he has studied aspects of team cognition and social networks on success in simulated space missions. He is a winner of INSNA’s Simmel Award for lifetime achievement. He has published extensively in anthropological, sociological, biological, aerospace, and marine science journals and was the founding editor of the Journal of Quantitative Anthropology, co-editor of the journal Human Organization, and is the author of Selecting Ethnographic Informants (Sage, 1990).

Review :
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.  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.  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. 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.  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.