Introduction

Navigating a large directed graph is an exercise in untangling a hairball. Take the complexity of a city and remove all of its urban planning — at Enigma, this is often the convolution we’re dealing with in our datasets. In the following paragraphs we explore how to unravel these tangled messes and get to tangible insights.

Force-Directed Graphs

The visualization above represents committee-to-committee transactions for the 2017–2018 election cycle (source: FEC). Looks like a lot to figure out, but here’s the kicker: this graph represents just 2,000 transactions, while the dataset has over 850,000 total committee-to-committee transactions.

This chart is a force-directed-graph (FDG), a clever tool for visualizing complex networks with a physics-based simulation. An FDG can be an effective tool for capturing the birds-eye view of a network topology. One can use it to spot local clusters, densities, and overall distribution.

While an FDG is a good summary of a network, it falls short of comprehensive. The above network is useless to study as an interface — we need to use network algorithms to whittle down to valuable areas of focus, and then use different visualizations for more thorough analysis. The case study below is an example of this process.