May 192010
 

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?

Fortunately, I had a list of full names plus email addresses for everyone who had signalled interest in the lecture before the beginning of term, so I created a short questionnaire in limesurvey and asked them a very simple question: whom do you know in this group? Given the significant overcoverage of my list – in reality, there are probably not more than 120 students who regularly turn up for the lecture – the response rate was somewhere in the high 70s. If you want to collect network data with limesurvey, the “array with flexible labels” question type is your friend, but keying in 220 names plus unique ids would have been a major pain. Thankfully, one can program the question with a single placeholder name, then export it as a CSV file. Next, simply load the file into Emacs and  insert the complete list, then re-import it in limesurvey.

Getting  a data matrix from Stata into Pajek is not necessarily a fun exercise, so I decided to give the networkx module for Python a go, which is simply superb. Networkx has data types for representing social networks, so you can read in a rectangular data matrix (again as CSV),  construct the network in Python and export the whole lot to Pajek with a few lines of code:


#Some boring stuff omitted
#create network
Lecture=nx.DiGraph()
#Initialise
for i in range(1,221):
Lecture.add_node(i, stdg="0")
for line in netreader:
sender = int(line[-1])
#Sender-ID at the very end
edges=line[6:216] #Degree-scheme
Lecture.node[sender]['stdg']=line[-8] #Edges
for index in range(len(edges)):
if edges[index] == '2':
Lecture.add_edge(sender,int(filter(str.isdigit,repr(knoten[index]))),weight=2)
elif edges[index] == '3':
Lecture.add_edge(sender,int(filter(str.isdigit,repr(knoten[index]))),weight=3)
nx.write_pajek(Lecture,'file.net')

As it turns out, a lecture hall rebellion seems not very likely. About one third of all relationships are not reciprocated, and about a quarter of my students do not know a single other person in the room (at least not by name), so levels of social capital are pretty low.  There is, however, a small group of 10 mostly older students who are form a tightly-knit core, and who know many of the suckers in the periphery. I need to keep an eye on these guys.

260 reciprocated ties within the same group

Finally, the second graph also shows that those relatively few students who are enrolled in our new BA programs (red, dark blue) are pretty much isolated within the larger group, which is still dominated by students enrolled in the old five year programs (MA yellow, State Examination green) that are phased out. Divide et impera.

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Jul 082008
 

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 only thing that is missing is a proper manual, but even this is not really a problem since Pajek’s creators have written a very accessible introduction to social network analysis that doubles up as documentation for the program (order from amazon.co.uk, amazon.com, amazon.de. However, Pajek has been under constant development since the 1990s (!) and has acquired a lot of new features since the book was published. Some of them are documented in an appendix, others are simply listed in the very short document that is the official manual for Pajek. You will want to go through the many presentations which are available via the Pajek wiki.

Of course, there is much more software available, often at no cost. If you do program Java or Python (I don’t), there are several libraries available that look very promising. Amongst the stand-alone programs, visone stands out because it can easily produce very attractive-looking graphs of small networks. Even more software has been developed in the context of other sciences that have an interest in networks (chemistry, biology, engineering etc.)
Here is a rather messy collection of links to sna software. Generally, you will want something that is more systematic and informative. Ines Mergel has recently launched a bid for creating a comprehensive software list on wikipedia. The resulting page on social network analysis software is obviously work in progress but provides very valuable guidance.

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Jun 302008
 

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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Jun 262008
 

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.

Ideally, we would build a huge database of articles, chapters, and monographs. However, this requires lots of research assistants, and so for the time being, we use the Social Science Citation Index, which covers at least the core journals. We are soon due to deliver a paper at a conference, so I started writing it up. I’ve already put some preliminary findings on co-publication in Politische Vierteljahresschrift (PVS), arguably the most important German political science journal, on the web. The summary is very short and perhaps not very surprising: it doesn’t happen on a large scale.

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