Federal Election Commission Campaign Data Analysis by Dave Fauth.
From the post:
This post is inspired by Marko Rodriguez’ excellent post on a Graph-Based Movie Recommendation engine. I will use many of the same concepts that he describes in his post in order to load the data into Neo4J and then begin to analyze the data. This post will focus on the data loading. Follow-on posts will look at further analysis based on the relationships.
The Federal Election Commission has made campaign contribution data publicly available for download here. The FEC has provided campaign finance maps on its home page. The Sunlight Foundation has created the Influence Explorer to provide similar analysis.
This post and follow-on posts will look at analyzing the Campaign Data using the graph database Neo4j, and the graph traversal language Gremlin. This post will go about showing the data preparation, the data modeling and then loading into Neo4J.
I think the advantage that Dave’s work will have over the Sunlight Foundation “Influence Explorer” is that the “Influence Explorer” has a fairly simple model. Candidate gets money, therefore owned by contributor. To some degree true but how does that work when both sides of an issue are contributing money?
Tracing out the webs of influence that lead to particular positions is going to take something like Neo4j, primed with campaign contribution information but then decorated with other relationships and actors.