"Mirror, mirror on the wall / Who is the fairest of them all?"
We are in a time of quantitative evaluations obtained by mining relationships . This is particularly true for the web (did you hear about Google?) but also for social networks (did you head about Facebook and Twitter?). The more your connections the more your value, your rank . Last week I was in a public commission to select a winning professor among seventy participants and the discussions about how and when to use bibliometric data to evaluate candidates was very hot, as you can imagine. BTW: this kind of evaluations must be taken cum grano salis, Albert Einstein could have lost a competition based on quantitative bibliometric data.
To relax a bit, I decided to apply the above concepts to the entire set of US representatives. The social-network rank ("PageRank") of each representative depends on sponsoring many bills which are in turn co-sponsored by representatives of high rank.
I went to the public website
opencongress.organd mined the contained data.
...and the winner is: Republican Scott DesJarlais. he has the highest Page-rank of US representatives.
Apparently, he is working with many people of different parties.
After the various representatives are mapped onto a two-dimensional surface by trying to keep highly-interacting representatives together (interacting means signing the same law proposals), one obtains a galaxy showing entities and relationships. Below the personal network of Rep. Ronald Paul.
More details on this analysis and the original data file can be found at the Lionsolver usage case
Warning: ranking people by social network analysis is fun but it must be interpreted with additional context information which may not be visible from these raw data. Use with moderation.