Social Network Analysis: Methods and Examples

Written very well to apply SNA to research

A very detailed and exhaustively referenced work on basic social network analysis (SNA) that delivers a lot of information in a fairly compact read. To be sure, this is not a book that constitutes easy reading, even for those who are used to spending their time reading textbooks. The text rarely wanders from being much more enjoyable to read than a dictionary, but I am pretty sure that this is how the author intended to convey the information covered.The only part of the book that I found disappointing was the lack of concentration on using statistical analysis to convert data into practical, usable information from anything but a theoretical standpoint. This book focuses much more on theory than it does on practical application or mining data for its full value.Overall, the book accomplishes its goal, albeit a rather limited one. If you have to start somewhere learning about SNA, this is definitely an excellent place to start. The reference section alone makes it worth the price.

This is an introductory text and a well-written one at that. The language, even though the subject is complex, is clear and direct and untainted by academic doublespeak. It begins at the beginning, defining what a social network is, which is quite a task these days because there so many structures dubbed “social networks”. The first chapter contains numerous examples and a lucid history of Social Network Analysis which should prove to be useful to newcomers.Subsequent chapters move logically to data collection; using descriptive methods, inferential methods; Social Network Analysis of Work and Organizations; Social Network Analysis in Crime and Terrorism; Social Network Analysis in Emotional and Physical Health; and Political Networks.The casual reader has the liberty to skip those sections that hold no interest to them. Students typically don’t and that’s where the author’s obvious insistence on clear language pays off: although the topics become substantially difficult and occasionally ponderous, the clear language renders the journey tolerable.For non-students who simply want to understand Social Network Analysis, this is an excellent introduction. But if you’re hoping to learn how to agglomerate data from social networks such as Facebook, this is not the book for you.Jerry

Overall, this is a good introduction to network analysis. I would be appropriate in an upper division course or perhaps a masters program. It could also work as a multi-week component of a doctoral level class, as well as a gentle introduction for those considering such an analysis. Competitive (similar) offerings include those by Barabási and by Borgatti.There is sufficient detail to follow along and execute a study with attention paid to measures such as density and centrality, the starting point for a descriptive analysis. Those needing details appropriate for academic research might be better served by other resources, such as those by Wasserman.

This is a lean, well-designed, lower level textbook with clean prose.The approach here is concept-heavy and math-light. The SNA examples from the fields of politics and terrorism are particularly rich.It seems to me that the balance between theory and practice as revealed in this book's pedagogical strategies is particularly deft. No mean feat!

Solid book about social network analysis basics from a social science perspective without advanced statistics calculations. Good for students interested in social network analysis encountered in social media, communications, education, and social sciences without delving into heavy statistical analysis. Seems to be written for an undergraduate level and is written as a text book with history, scenarios, and review questions at the end of each chapter. A lot of real world examples/areas are included.The book would have benefitted from having color charts and graphs. All of the graphs are grayscale and it can be hard to decipher between light gray and dark gray when looking at a complicated chart with a lot of points/data.