This section describes how I began my shift from metaphor to method in my use of network analysis. It describes a variety of sources and offers a few pointers for others who might wish to follow this path.
When this research began, my knowledge of network analysis was rudimentary. I had seen some network diagrams and vaguely imagined that my credits data from the TCC annual could be made to look like that. Thus, while designing the database and arranging for the data to be input, I was also trying to teach myself a new academic discipline. The progress reported here remains at an elementary level. I have a better idea now of things that might be done; but what has been done so far is, truth be told, still very basic, indeed.
Step one was to read some popular books on the subject: Duncan Watts (2003) Six Degrees: The Science of a Connected Age and Albert-Lazlo Barabasi (2003) Linked: How Everything is Connected to Everything Else, for example. These books taught me that social network analysis is a subset of a larger field called network analysis, whose mathematical principles apply to transportation and power grids, the Internet and protein cascades as well as social networks. That was exciting. But while books in this genre were good for pumping up my interest, they didn’t teach me how to do network analysis.
An old friend, sociologist James Ennis put me on to UCINET, academia’s most widely used social network analysis software, and searching the Web to learn more about that program brought me to Robert Hanneman and Mark Riddle (2005) Introduction to Social Network Methods (available online at http://www.faculty.ucr.edu/~hanneman/nettext/). This text whetted my appetite but was plainly designed for students with professors handy to answer their questions. As an independent scholar, I was, or so it felt to me, left working out too much for myself by trial-and-error.
Pursuing other leads brought me to Stanley Wasserman and Katherine Faust (2005 ), Social Network Analysis: Methods and Applications, a frequently cited work that provides a very thorough grounding in the mathematics of network analysis and its application to social networks\and is tough going for a newcomer who is still trying to resurrect what he learned in undergraduate courses in logic, calculus and probability. I ran into a similar problem when I purchased a copy of Mark Newman, Albert-Lazlo Barabasi, and Duncan J. Watts (2006) The Structure and Dynamics of Networks.This collection of classic papers is a deep introduction to the history of network analysis and nicely complements Lin Freeman (2004) The Development of Social Network Analysis: A Study in the Sociology of Science, which provides a wealth of the human detail concealed in more abstract introductions to the field. That said, it, too, is tough sledding for a beginner unfamiliar with the relevant mathematics.
Today getting started is easier than it was even just three years ago. Newcomers to the field can start with Alexandra Marin and Barry Wellman (2009) “Social Network Analysis: An Introduction” forthcoming in the SAGE Handbook of Social Network Analysis. The year 2010 also saw the publication of three new textbooks that target advanced undergraduates and graduate students taking a first or second course in network analysis: David Easley and Jon Kleinberg (2010) Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Matthew O. Jackson (2010) Social and Economic Networks, and M. E. J (Mark) Newman (2010) Networks: An Introduction. All are written by masters in the field and are relatively accessible to readers whose math is at my recovering undergraduate level. I would still, however, need to learn not only basic network concepts but also how to use a software package to analyze my data.
The book that turned my life around and made this research possible is W. de Nooy, A. Mrvar, and V. Batagelj (2005) Exploratory Social Network Analysis with Pajek, a book that lives up to the promise of its blurb and is, in fact, ” the first textbook on social network analysis integrating theory, applications, and professional software.” The concepts used in my research were, if not first encountered, first understood, at least in part, by working through this book. For an independent scholar there was also the added advantage that Pajek, written and updated by Andrew Mrvar and Vladimir Batagelj and their colleagues at the University of Ljubljana, is freeware and easily handles networks with thousands of nodes.