1. What data sources do you use?

2. How do you construct these networks?

For all three networks, nodes (a.k.a. circles) are people and links (a.k.a. lines) between nodes indicate that two people were on a common e-mail thread. The size of a node shows the number of e-mail threads in which a person participated. The size of a link denotes the number of shared e-mail threads between two people. Each node is algorithmically assigned to a community (a group with a disproportionately large number of links among them), which is shown by node color.

3. Why did you create this tool?

At the MIT Media Lab Macro Connections group, we build tools to transform data into stories. ClintonCircle was not created to visualize these emails, but it is a fork of a tool called Immersion that we launched in 2013 and that helps people visualize and understand the networks they weave through email interactions.

Some of the other tools we have built include the Observatory of Economic Complexity, which is the world’s leading site visualizing international trade data, Pantheon, a project exploring our species collective memory, and DataUSA, the most comprehensive visualization of US Public Data. We build all of these tools to improve people’s ability to understand and explore data, and to democratize the benefits of data exploration and visualization.

4. Who is ClintonCircle built for?

5. Who are you?

Our team is composed of Kevin Hu and Jingxian Zhang (graduate students at the Macro Connections group at The MIT Media Lab), and led by Professor César Hidalgo.

Kevin Hu is a doctoral candidate working on tools for accessible data visualization and analysis. Jingxian Zhang is a masters student creating e-mail network visualization tools for organizations. Professor César Hidalgo, PhD, is the Principal Investigator and Director of the Macro Connections group at the MIT Media Lab.

We thank Jeremy Rubin for helping formulate the initial idea, and Daniel Smilkov and Deepak Jagdish for working on the original version of Immersion.

Previous Post
Next Post