Social network analysis (SNA) is grabbing the attention of many scientists in psychology, anthropology, and sociology, beyond its traditional disciplines. In part, this popularity is due to the emergence of social networking Web sites, such as Facebook and LinkedIn, and the myriad of social media networks, from Flickr and YouTube to WordPress, Blogger, and Twitter.

It comes as no surprise, therefore, that we have begun to see the emergence of introductory textbooks such as this one that appeal to a broader audience than traditional textbooks in the field do [1,2]. Prell has written an excellent overview of the SNA field, discussing many of its key ideas from the early beginnings of sociometry in the 1930s to more recent work on statistical models. Of course, you cannot get a detailed account of every minute detail in a 250-page monograph (especially when compared to Wasserman and Faust’s 850-page tome [1]), but the author has managed to introduce most key ideas in SNA and present them in the proper context for practitioners.

The book is organized into three main parts: an introductory overview of the SNA field (“Background Understanding”); a survey of analysis tools and techniques (“Levels of Analysis”); and a final chapter on statistical models for social networks (“Advances, Extensions, and Conclusions”).

The first part of the book introduces readers to the terminology of SNA, the use of graphs to describe social networks, and the algebraic representation of graphs by means of adjacency matrices (incidence matrices for affiliation, or two-mode, networks). This is followed by an interesting account of the history of SNA. Prell focuses on the evolution of the different strands that led to modern SNA, including social psychologists such as Jacob Moreno, Kurt Lewin, and Alex Bavelas; anthropologists such as Alfred Radcliffe-Brown, W. Lloyd Warner, and Max Gluckman; and sociologists who, somewhat surprisingly, were not involved in the initial development of the field but who made significant contributions sometime later. Examples of the latter include Harrison White and his outstanding PhD students at Harvard, an impressive roster of well-known names in the field that includes Mark Granovetter and Philip Bonacich. This introductory part concludes with a 30-page chapter on how to conduct a social network study, from preparation (the development of hypotheses and questionnaires) to execution (sampling and gathering data).

The second part of Prell’s monograph focuses on the analysis of results. Prell has chosen to organize her discussion around different (and complimentary) levels of analysis: actors at the individual level; dyads and triads as the local building blocks of social networks; whole subgroups within the network; and the network as a whole. At each level, the most relevant concepts are introduced and commented upon, including the different measures of centrality used to assess the importance of individual actors; the importance of structural holes, brokerage roles, and homophily in ego networks; the potential insight provided by the census of dyads and triads in a network; the different kinds of groups (components, cliques, k-cores, and lambda sets) and communities (clusters) that can be detected within a network; and overall network metrics such as density, diameter, and average path length. A final chapter on position and role analysis closes this excellent roundup of SNA tools and techniques. In this chapter, structural and regular equivalence are discussed from an unusually pragmatic point of view.

The last part of the book includes a brief--and perhaps too shallow--bird’s-eye view of statistical models for social networks. Given the author’s outstanding ability to clearly explain in layman’s terms whatever concepts are thrown at her, I would have rather read a more thorough explanation of the exponential random graph models (such as the p* model) and agent-based models for longitudinal analyses that are briefly described in this final section.

In spite of the minor lack of detail and some typesetting mistakes in the sparingly few equations that accompany the text in separate sidebars, I would heartily recommend this book to anyone interested in getting acquainted with SNA. Its pragmatic point of view--with recurring hints on how to perform different kinds of analysis using UCINET, a popular software tool for SNA--makes this book especially suitable for practitioners. Readers might want to complement it with a more theoretical monograph, such as the recent book by Charles Kadushin [3], one of the pioneers in the field. Kadushin analyzes the psychological foundations of social networks, such as support/safety, effectiveness, and status/rank seeking, and provides a more holistic view of the field, beyond the rather mechanistic perspective offered in a survey of the analysis techniques that can be applied to social networking data.