I’m teaching a lecture course on Political Sociology at the moment, and because everyone is so excited about social capital and social network analysis these days, I decided to run a little online experiment with and on my students. The audience is large (at the beginning of this term, about 220 students had registered for this lecture series) and quite diverse, with some students still in their first year, others in their second, third or fourth and even a bunch of veterans who have spent most of their adult lives in university education.

Who knows whom in a large group of learners?

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Our project on social (citation and collaboration) networks in British and German political science involves networks with hundreds and thousands of nodes (scientists and articles). At the moment, our data come from the Social Science Citation Index (part of the ISI web of knowledge), and we use a bundle of rather eclectic (erratic?) scripts written in Perl to convert the ISI records into something that programs like Pajek or Stata can read. Some canned solutions (Wos2pajek, network workbench, bibexcel) are available for free, but I was not aware of them when I started this project, did not manage to install them properly, or was not happy with the results. Perl is the Swiss Army Chainsaw (TM) for data pre-processing, incredibly powerful (my scripts are typically less than 50 lines, and I am not an efficient programmer), and every time I want to do something in a slightly different way (i.e. I spot a bug), all I have to do is to change a few lines in the scripts.
After trying a lot of other programs available on the internet, we have chosen Pajek for doing the analyses and producing those intriguing graphs of cliques and inner circles in Political Science. Pajek is closed source but free for non-commercial use and runs on Windows or (via wine) Linux. It is very fast, can (unlike many other programs) easily handle very large networks, produces decent graphs and does many standard analyses. Its user interface may be slightly less than straightforward but I got used to it rather quickly, and it even has basic scripting capacities.

The Missing Manual

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More preliminary findings on Social Networks in Political Science: from our analysis of collaboration patterns in the British Journal of Political Science (BJPS) and Political Studies (PS), we conclude that co-publication is much more widespread and intense than in Germany (not a huge surprise). Yet, at least on the basis of these two journals, collaboration networks in British political science look rather fragile when compared to the sciences. Obviously, further research is needed.

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Like most social scientists I am a little bit obsessed with social networks. I’m also interested in the sociology of knowledge, which is a little more original. So some time ago, a colleague and I embarked on a project called “Networks in Political Science”, which rather unsurprisingly tries to apply network analysis to publications in Political Science. Our basic idea is that everyone seems to take subfields, theoretical schools and even citation circles for granted, but unlike in some other disciplines, little empirical work has been done so far. More specifically, we want to identify

• highly cited articles that form the core of subfields
• individual influential scholars
• sub-networks of scholars that cite each other and/or collaborate frequently, thereby forming an “invisible college” and
• individuals that are able to bridge sub-discplinary divides by publishing in a whole host of subfields.

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