Recently I have been involved with a project mapping relationships between countries in terms of a social network. There are a lot of social network analysis packages around; I prefer Python’s NetworkX largely because I’m already so used to Python.
The first thing everyone wants to see when doing sna is the network graph…understandable of course as they look pretty visually attractive and are a welcome respite from a field (political science) which is dominated by text. However as we all know sna graphs can also be a bit misleading, unless you are very good at reading them. The fact that node position doesn’t (necessarily) mean anything is a bit of a disadvantage, and once you have more than a few nodes actually understanding link patterns is essentially impossible. Using a classic layout for my country relations SNA for example gives me this:
Even with degree included as node size and colour I still don’t find it very informative. One way of improving the situation is to give some meaning to the node position, which is of course especially easy with countries. Displaying a sna as a world map has two advantages in my opinion: everyone knows the names of a lot of the countries (which saves you having to label nodes), and you can also get a quick handle on any geographical patterns.
Python’s Basemap module can be easily combined with NetworkX. The key is to build your NX graph’s ‘pos’ list as you are building your overall node list, using Basemap to transform node coordinates. Of course you will need a list of country longitudes and latitudes but there are plenty of those available.
Hence when I am building my overall graph, I do:
x,y = m(lon, lat) G.add_node(country) pos[country]=(x,y)
where m is any Basemap projection and lon, lat are the coordinates of the country in question.