Gephi is a very useful tool. I’m very much looking forward to the new release that seems always on the horizon. In the meantime, though, every time I open Gephi it crashes, and then I dive down a long rabbit hole of trying to re-write the program code, and then I get angry and go home. So I’ve been delighted to find that a combination of R (for manipulating and analyzing the data) and d3.js (for visualizing the data) does most of the work of Gephi with much less frustration.
I’ve been using Kieran Healy’s work on Paul Revere and network centrality and applying it to a cohort of men who served on the boards of philanthropic organizations in New York in the 1840s. I am particularly in the officers General Relief Committee for the Relief of Irish Distress of the City of New York. These men – Myndert Van Schiack, John Jay, Jacob Harvey, George Griffin, Theodore Sedgewick, Robert B. Minturn, George Barclay, Alfred Pell, James Reyburn, William Redmond and George McBride Jr. – were deeply politically connected, but don’t seem to have had much of a relationship to one another.
Healy’s script, and Mike Bostock’s d3 blocks helped me to build a matrix which tracked relationships between philanthropists via organizations, making note of the number of organizational connections that different pairs of men shared; and another matrix which tracked relationships between philanthropic organizations and social clubs via philanthropists, making note of the number of men that each organization shared. I used the former to build a force-directed network diagram, which, in combination with some R based analysis, suggests that while the New York Famine Relief Committee officers didn’t often serve on other committees together, they shared other social connections.
For example Jonathan Goodhue was not a member of the famine relief committee, but served on other committees with nearly every General Relief Committee officer. Of the New York famine relief committee members, Jacob Harvey was the most centrally connected member. This data has pointed me in some new archival directions, but also give a much better sense of the ways in which people were connected to one another than comparable textual descriptions might do.
I also built a network diagram showing relationships among different newspapers reporting on the famine, which cluster newspapers more inclined to cite each other.