The next software package that we were introduced to in the DALMOOC was Gephi, which is an open source tool for conducting social network analysis. I found Gephi an easier tool to use than Tableau, and it was fairly straightforward to load the sample data that was provided and start analysing it.
These were the results of my analysis to determine the density and centrality measures of each dataset :
For the example_1 dataset:
For the example_2 dataset:
For the CCK11 dataset (Twitter network):
For the CCK11 dataset (blog network):
These were the results of using the Giant Component filter, and then determining the modularity for each dataset:
For the example_1 dataset:
For the example_2 dataset:
For the CCK11 dataset (Twitter network):
For the CCK11 dataset (blog network):
It was also fun to play around with the various network representations, and the options for partitioning and highlighting various properties of the network. This is the example_1 network with a few changes made to it: it’s in the Fruchterman Reingold representation, nodes are sized according to betweenness centrality, labels are turned on, and each community is a different colour
Here’s the example_2 network with similar changes:
And for the CCK dataset (Twitter network):
And finally the CCK dataset (blogs network):
I found these exercises a useful way to get some experience with social network analysis, and I have some ideas of how I could use Gephi in a project that I’m working on.