Dear all,

This is a reminder for our upcoming Historical Network Research Afternoon Tea lecture on Thursday March 10. The seminar will start at 4:00 pm CET (10 am EST / 3 pm BST) and finish one hour later.

Our speaker will be John R. Ladd, visiting Assistant Professor in Data Analytics at Denison University. He will present his talk entitled “Comparing Networks of Biography: Six Degrees of Francis Bacon and John Aubrey’s Brief Lives” (see abstract below). 

If you would like to participate, please register via Eventbrite before March 9, 2022. You will receive a Zoom link by email prior to the lecture. 

We hope to welcome you online on March 10! Meanwhile, if you have any questions please contact us at events@historicalnetworkresearch.org.

Best wishes,

Aline Deicke and Ingeborg van Vugt

Comparing Networks of Biography: Six Degrees of Francis Bacon and John Aubrey’s Brief Lives

John Aubrey devotes a distinct subset of the biographical sketches in Brief Lives to famous authors of his recent past, helping to cement a developing early modern canon. By contrast, Six Degrees of Francis Bacon contributes to a tradition of expanding canons by reorganizing its data—drawn from the Oxford Dictionary of National Biography—as a network of early modern relationships. Using role detection, in contrast to traditional community detection techniques, I consider the status of authors within Aubrey’s single-source network of biographies and the much larger secondary-source-based SDFB network. I argue that Aubrey presents authorship as a series of social interactions and collaborations, and likewise early modern social networks reveal that the work of authorship is distributed across a system that includes writers and a diverse group of other professionals. Drawn from my book project, Network Poetics, this talk will cover the generation of networks from biographical sources, the uses and limitations of the SDFB data set, and the application of role detection to small and large historical networks.

A portion of the SDFB network, using role detection for node color