I am coming to understand what is interesting in any particular network visualization by looking at many network visualizations.

Using NodeXL, I have made several maps of  social media networks of people talking about several topics of interest from current events to conferences I attend.  You can find a collection of them on flickr.

I look at these images and look for differences in the number of big clusters: some images have a “double yolk” – that I propose is a necessary (but not necessarily sufficient) condition of defining a topic to be “controversial”.  These two cluster networks have two well defined populations who lack much if any connection across the divide to the “other” side.

Some networks are highly populated but sparse, these are often the networks that form around brands where a central account tweets and is retweeted by many.  But these many lack much connection to one another.  So these brands form broadcast networks, not communities.

Some networks are dense single clusters with few if any “isolates”.  Isolates are people who say a term, and thus appear in the graph, but have no connections (follows, replies, ore mentions) to anyone else in the graph (at least as observed and reported by twitter at that time).  These dense clusters without isolates are topics where everyone is in-group.  Examples, like “scrm”, are technical and business terms that identify medium sized populations with high levels of density.

Have a look and see what patterns you can find.

[flickrset id=”72157622437066929″ thumbnail=”square”]