In the first two posts in this series, I described how to use Gephi to visualise various different views over a personal social network in Facebook using data pulled from a Facebook account using the Netvizz application. This was followed by a post describing how to and run some simple social network analysis statistics over the network. In this post, we’ll look at another powerful analytic tool provided by Gephi: clustering. [Note that after publishing this post, a far better way of visualising the clustered groups was suggested to me – find out more in the next post in this series.]
Clustering is a mathematical process in which different elements in a particular set are grouped together based on certain similarities between the different elements. That is, like is grouped with like. In a social network, clustering algorithms typically group together individuals who form “subnetworks” – for example, subsets of the the whole population who all know one another.
Being able to identify clusters within a social network allows you to identify “subnetworks” within that larger network. In Gephi, a graph can be clustered by running the Modularity in the Statistics panel – so here’s what I get when I run this measure over my Facebook network:
The Modularity clustering tool identifies several different clusters, (that is, groups or classes) within the network as a whole, associating each node with one of the groups.
One you have run the tool, you can view the cluster each node has been associated with by using the Modularity Class Ranking parameter; I find that the colour mapping is the most effective:
You can inspect the size of the various classes in a crude fashion via the Filter panel: select the Modularity Class option from the Partition folder in the filter Library and drag the filter to the query window. If you click on the Partition column element, you will be presented with a window showing each pf the partition classes:
If you now filter on one of the partition classes, and switch on the node names, you can see which nodes have been clustered together. Looking separately at two of the largest clusters in my Facebook network, I can see two OU clusters:
and a North America/Canada dominated ed-tech cluster (which also includes some BBC folk via Bill Thompson…):
(Note that it is possible to highlight/filter on more than one cluster within a single filter (on a Mac, fn-click allows you to select multiple individual clusters).
The Modularity statistic thus provides us with a powerful tool for identifying subgroupings within a social network. So why not try it using your own data – are the clusters that are identified meaningful to you?
PS a far better way of visualising the clustered groups was suggested to me – find out more in the next post in this series.