Continuing my saga of visualizing Koornk social network I decided that obvious next step is to map out who talks to who and how much. For this task I used excellent Python library NetworkX that uses pygraphviz to draw the pretty pictures in the end.

Just to explain what you’re looking at:

- I downloaded all public conversations from Koornk and filtered out to the ones that use @ somewhere to reference someone else
- You need to all-together reference or be referenced 60 times to get on the list (70 people from 1606 made it)
- From those 70 people, if two of them talked more then 40-times they got a line between each other
- Line thickness is then calculated based on how much they talked to each other
- Circle size around each person tells you their cumulative chatter towards others

Fun statistic: about 22% of all message looked at (N=81990), contained @ reference

**Pretty pictures**

It turns out that there’s a smaller group of very vocal people within this view, so we naturally want to see zoomed version:

**Lessons learned**

- It takes about two days to properly get a hang of NetworkX library to draw something like that. It doesn’t mean you know anything about graph theory, but at least you can start drawing pretty pictures.
- Pictures are fun, but next step is probably interactive Flash diagram that allows you to explore these relationship for yourself
- Throwing around these data structures actually takes a few seconds on modern PC. Finally something meaningful for it to process.
- I wonder how much work would be to properly plot something like this for a subset of Twitter relationship if I maybe drink from their fire-hose long enough. Maybe Gnip guys can fill up a few Terabytes of Hard Drives with back log, if they have it and we start crunching this. (I’m assuming that there’s already a post-graduate student somewhere that’s doing exactly this)