|Original Bulletin Board Graph|
ToolsIn short, Python and Gephi -more details later.
The NetworkI ended up with a network of 79 nodes and 345 edges. The node labels were extracted keywords from the threads -edges were people who posted between threads.
AnalysisFirst question is there anything to look at? Any Structure? One way to tell is by constructing a random graph and comparing it's properties with those of our graph -this can be done, a little tediously, with Gephi.
Our graph has an average degree of 8.73, a shortest path length of 2.22 and an average clustering coefficient of 0.634. In comparison a random graph with same node and edge counts has a degree of ~4.4 a path length of 2.2 and the clustering coefficeient of 0.144 degree is considerably lower than our graph, so nodes are more connected and, although the path length is similar to a random graph the clustering is higher, indicating small world properties -which gives us something to look at.
There are a 3 super nodes in the graph :
|clarkson, people, one, bbc||1015|
|people, like, money, would||650|
|clarkson, one, public, sector||343.|
People in the UK can probably guess the reason for the first and third nodes. The comment was so outrageous that perhaps most people felt that they had to say something about it, and abnormal relationships in the graph could be created. After removal of the clarkson nodes the graph is as below:
|Clarkson free graph|