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
Martin Everett is professor of social network analysis and co-director of the Mitchell Centre at the University of Manchester. After studying mathematics as an undergraduate and at masters level, he undertook a doctorate in social network analysis. He has over over 30 years of experience of social networks and has contributed to over 100 publications. He is a co-developer of the network analysis software UCINET and is co-editor of the international journal Social Networks. He is a past president of INSNA (The International Network for Social Network Analysis) and a Simmel award holder (the highest award given by INSNA for contributions to research). He has regularly given keynote speeches at international conferences and has consulted to both government agencies and private companies on the use and application of social network analysis.
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
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
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 passe; 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