I ended up exploring “Using Metadata to Find Paul Revere.” Personally, I am not the most thrilled to see graphs and networks again after algorithms, but it is still fascinating to see applications of graphs that I would never even consider and imagine. Essentially, “Using Metadata to Find Paul Revere” uses a dataset of 254 colonial figures (i.e. John Adams) and their memberships in seven organizations which creates a 254 by 7 matrix. Using some linear algebra, this becomes a 254 by 254 matrix which can easily be represented as a graph from the transposed matrix. In the graph, the nodes represent a person, and each edge corresponds to two people being members of the same organization. Moreover, the blog post uses creativity to imply that this graph can be used to locate “agitators” in the colonies in the 18th century. Although it is apparent just from the graph that there are suspects with many connections, the project also uses more linear algebra via eigenvectors to calculate the centrality score for each person which unsurprisingly ended up being Paul Revere.
As someone who has taken both linear algebra and algorithms, I was not expecting to see graphs and matrices in my digital humanities class. For me, I find the graph to be the rather interesting and unlikely application of something I learned in prior classes. The dataset is rather simple, and I am not sure how in engaging this project would be for people who have not been exposed to graphs and matrices in the past.