#gary tang

This is the movie version of the seven episode miniseries

Project born at the Campaign Finance Datafest at Stanford:

Here we create graphs where politicians or political organizations (nodes) are connected by mutual donors (edges). The edges are weighted by the number of mutual donors; edges with greater weight are more "attractive". Different nodes also exert a small "repulsive" force on one another. By simulating these "attractive" and "repulsive" forces on the nodes, these graphs begin to take shape over time and form into clusters.

Not only do these graphs provide interesting insight into the clustering properties of politicians and political organizations, they may be used to create a recommender system for donors; i.e., suggest politicians a donor may be interested in donating to according to previous donations. When a donor selects a node (politician/political organization) to donate to, we can find the neighbors with one degree of separation to the node in a graph. These neighbors are then recommended to the donor and may be ranked in various ways (via popularity of neighbor, edge weight etc).

Tech info and data sources:

Graphs created using the 2012 Campaign Finance Data from OpenSecrets.org.

Data cleaned and analyzed using a combination of Python and R.