A few indications on the clustering by age and location:
Clustering works on the topology of the network (= the patterns traced by how edges and nodes connect). The attributes such as age, location etc. do not intervene in the process of community detection.
So, what is possible to do for age is:
- cluster your network with the Louvain algo.
- color-code the nodes according to the Louvain community they belong (you do that with the partitioning tab).
- resize the nodes according to age (with the ranking tab: the bigger the node, the oldest the node)
- visually inspect the resulting network: is one community (say, represented by red nodes) composed disproportionately of big nodes? Or is the distribution of size uniform across communities? (from what I could see the second option is the correct one). So I'd say that age does not correlate with the organizing of the topology of the network in communities. In other words, the age of the agents of your network does not seem to account for their clustering in communities.
For location, that's more tricky. I'll give you just the basic steps:
- outside gephi: use a geocoder service to convert the attribute "location" into latitude and longitude coordinates.
(
http://developer.yahoo.com/maps/rest/V1/geocode.html)
(
http://blog.jonudell.net/2007/08/10/exc ... dventures/)
(
http://code.google.com/apis/maps/docume ... geocoding/)
[here you should be aware that the users you are interested may have provided fantasist answers, you'll have to filter that out etc.]
- I would also try to find a tool which would convert these coordinates into a country name! Could not find it with a quick search, though. Anyone?
- back to gephi: include these two new attributes, lat and long.
- use the geo layout plugin of gephi to map your nodes according to their spatial coordinates
(
http://gephi.org/2010/map-geocoded-data-with-gephi/)
- partition / rank your nodes according to their age, reputation, etc... and get to see if it overlays in any manner with the geographical layout. If you succeeded in transforming your lat / long data into country names (again, I'd be very interested if anyone has a pointer to a web app which does that!), then you could:
* layout your network with openord, as before
* partition your nodes according to countries.
* viz the map: are nodes from particular countries close from each other?
Alternatively, actually even cooler:
* layout your network with openord, as before
* color rank your nodes according to their lat
* color rank your nodes according to their long (using the same colors as lat)
=> I did not try, but I suspect that if your nodes are structured according to a geographical logic (closer nodes in the network are also closer in their spatial location irl), your network will look like a heat map. If not (the network is a mash-up of colors), then it means that the location of the node does not contribute to the structure of the network. Which is the likely result I think, given the nature of your network.
Best,
Clement