Back in the 1990s, there was a joke that every actor was connected by at least six degrees to Kevin Bacon, which makes one wonder about social networks. It is in this spirit that a group of history professors, set to study the social networks of the medieval times, created Six Degrees of Francis Bacon.
The project was co-founded by literature and cultural studies professor Christopher Warren and has three other PIs. As it is a digital humanities project it is open source, the project’s repository is available on GitHub, and it is open to take contributions of scholars worldwide. The website of the project is written in Ruby on Rails, Angular, D3 and the network is written in R.
The project looks very simple, each node represents a person and each vertex represents the existence of a relationship between those people. The black edges are nodes that are confirmed relationships between people, which are added by scholars. The grey nodes, on the other hand, are statistical inferences generated by a computer. The nodes are scattered across the screen with Francis Bacon’s node having a prominent color, but not on the center of the screen. The central nodes are historical characters who have the most direct relationships.
Users are allowed to click both vertexes and edges. If a vertex is clicked the system shows a network of people who had a relationship with the person selected. If one clicks an edge they will be shown both characters’s lifespan and a number which indicates how sure the software is that the two characters actually interacted. While the website does not provide that much information on each character, one can see their social network by clicking the nodes and the certainty that the relationship existed by clicking the edges. This provides an engaging experience which helps you to understand the relationships people had in that time and get a sense how different historical characters were connected. Just like the Kevin Bacon joke, one can see what is the degree of separation between Francis Bacon and other historical figures, which is a nice activity.
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I’m fascinated by the description you give concerning the behavior of edges when they are clicked. It shows that the programming language used to enable such interactions is probably suitable for machine learning as it can make inferences from the data it has collected. I’m tempted to say that some Python was used to achieve this but not sure if that’s the case. Nevertheless, I’m intrigued to look at the source code of the project on GitHub.