Community detection with the offshore leaks data
Someone on the Internet ran an algorithm using iGraph that automatically assigns nodes to clusters based on whether they seem to be in a real-life community with each other or not.
What does this mean? A good way to understand this is using your friends on Facebook. As you go through life you meet people in groups, and these groups form separate clusters or communities. For example, your friends from high school form one cluster, your friends from college another cluster. When you start a new job all the new colleagues you add on Facebook become a new cluster.
One of the ways to visualize this is by clumping together nodes that are part of the same cluster and assigning them the same color. That's what this person has done.
For example, the majority of the addresses in Mexico are all in San Pedro Garza Garcia, the wealthy suburb of Monterrey where Dionisio Garza Medina lives. Those addresses and the Grupo ALFA subsidiaries in the database, along with Garza Medina himself, are part of the same cluster.
If there are other clusters in Mexico then by visualizing them in this way we'd be able to see fairly quickly where they intersect, allowing you to see the big picture.
It gets even more interesting when you add time series. Because we have dates for the roles then we can build a progressive series of visualizations that show how the communities wax, wane, merge and divide over time.
For example, if when looking at the progression of visualizations we see that a cluster with a strong presence in Switzerland appears to explode in size at some point in time, then we can dive into the data and see that it happened when person X became the director of one of the Swiss entities. That could indicate that X was the go-between responsible for dealing with Portcullis TrustNet and that he or she hooked up all the other companies.
