Robert Brissey

Lab 5 Write Up

English 8120 – Dr. Thomas



This lab was possibly the most technologically intensive lab of the bunch this year. And accordingly, the largest issue for me was technologically based. Namely, Gephi uses a considerable amount of computing power, so I had to make sure I had almost everything else closed, or my models would take a really long time to stabilize. The only real issue that I encountered is that my “Preview” tab often refused for whatever reason to display my model. This is why I have a few renditions of certain graphs in my lab folder, because I had to save them in before I could view them. In a related issue, I had two of my graphs, graph 3 and graph 5.2, become incapable of being saved in the normal way. To remedy, I had to do the “screenshot” option to be able to use the graphs. This roundabout strategy to get the desired results demonstrated an old rule of technology: if one cannot get in through the front door, there is more than like a rear entrance.

The graphs, to my untrained eyes, are still somewhat esoteric in the information they display. However, from what I was able to discern after re-reading the steps I did to complete them, I can say that the weighted degree became obvious in function in graph 1. The largest names upon the graph are clearly those with the highest level of degree, or the most interconnectedness as compared to all other nodes. Graph 2 was similar to graph 1 largely, but with a different overall structure that showed concentrated communities of nodes. Graph 3, which is unfortunately in the lowest resolution due to the screenshot, shows that hard to define quality of betweeness centrality, which is only clear when one considers it in terms of diameter. The differences between graph 3 and graphs 1&2 were largely found in the nodes, rather than edges. The communities described in graph 2 were consolidated further by running the network diameter function, which showed similar nodes, as in graphs 1 and 2, being more weighted (shown by being a larger node in the graph), but to describe its location relative to the community as opposed to its weighted degree. Graph 4 was possibly the least useful if the most aesthetically pleasing. Despite using “Noverlap”, there is still a considerable amount of overlap. Furthermore, the labels seemed to disappear. Upon further consideration, it is entirely likely that I did not complete some step in graph 4, though I went back through it several times to no avail.

Graph 5, created from my data, was considerably simpler. There were only 5 nodes, and 3 of the 5 nodes were weighted exactly equal. The largest node, predictably, was Sherlock Holmes’s, as he was the only person who addressed each and every person individually within the chapter from “A Study in Scarlet”. Now, it might be a matter of some speculation to consider, this particular tale being the chronological first in the series, if perhaps Dr. Watson might prove himself to be equal to Mr. Holmes in terms of weighted centrality in later tales. By this I mean, Dr. Watson was “new” to the game of deduction at this stage in the novel, and therefore would presumably be more active and involved in chronologically later stories. Also, if I had taken the short novel as a whole instead of one chapter, perhaps Holmes’s centrality would have grown by a magnitude or two.

Network analysis allows a visualization of massive quantities of data so as to allow the researcher to get deep insights into the relational aspects of seemingly unrelated quantities. This visualization would very likely lead to several types of research: the researcher could to focus on the main nodes as a point to begin research, or just the opposite, focusing on the isolates, or just as likely, focusing on the connective nodes that bridge two seemingly polarized bodies of research (or whatever was being graphically represented). The advantages, especially when one views literature in terms of communities, are evident. It allows, much more so than traditionally literary historicity based on canonical chronology, to see the development of whole literary movements based on who was central, who was only passing through, and who was a decisive connection.