I think there was some kind of error in my data importing process. While I agree with the topic tag connections, there are duplicate tags that will list different permutations of the same words. I would have to do more investigation to figure out how to eliminate the duplicates. They make the graph more cluttered than I would like.

The Yifan Hu layout algorithm seems to bifurcate the data based on the amount of connections between tags; in the chart that I’ve created that filters the ID network by relationship to James Joyce, the network produced by the Yifan Hu layout is two wings connected by a central node of the novel, which is a convenient visual to conceptualize the two main written works related to Joyce. When the full data set is arranged in the Yifan Hu layout, the duplicate data creates a lot of visual clutter as the data separates into grouped islands; I’ve included a screenshot of one island, where I can note the centrality of the term “poetry,” although the rest of the data is a bit jumbled.

Given the limits of visuality in the Yifan Hu arrangement, I was pleased with the image generated by the Fruchterman Rheingold layout, which created a modular arrangement that flexes upon rearrangement to maintain the relationship between nodes. I felt that this model was more visually intuitive (although that may be partially because of the visual limitations of the duplicate nodes) because it kept the data contained and connected rather than dispersing it into discrete islands, which I think models the conceptual relationships between topics.

I think this network visualization is a useful method for periodical studies (given the future ability to import data without error…) because it reveals relationships that are not evident upon close reading, but, once revealed, may help guide/inform close reading. I think I could benefit from this procedure as a way of giving a fresh set of eyes to the same brain.