Contrary to my experience in the previous labs for this course, working with Gephi ultimately left me with less issues surrounding the use of the tool and more unsure about how to interpret the data that were produced. My greatest tech problem (which I will say upfront may have resulted from my incompetence rather than the program) came when attempting to locate “Geo Layout” plugin on the layout panel. If my life depended on finding it, then I would be a dead man. I was, however, able to locate the latitude and longitude options, and was able to select both and apply them to the graph. I don’t know if this resulted in the appropriate graph for this step, but it was the closest I could get to following the instructions. Though I was able to improvise a bit to make adjustments to the graph and work around some of the tutorial, the ambiguity of the end result restricts my ability to determine whether I actually learned anything during this process. The challenge with Gephi, more than the other tools that we have worked with thus far, is that so much of the calculation and distribution of nodes and edges takes place behind the scenes. It makes for fancy, colorful graphs to be sure, but it also makes attempts at troubleshooting and reflection somewhat problematic. The use of this program, ultimately, may be better utilized with an approach focused on mastery rather than introduction, which is, of course, beyond the scope of this class.

I didn’t have any trouble creating a graph with the data points I created from Lady Windermere’s Fan. I attribute this largely to the fact that the sample data sets that we downloaded were laid out in an approachable manner. Sure, the long series of numbers are nearly sufficient to generate a panic attack, but the use of the first line of the spreadsheet to tell both myself and the computer how each of the following nodes and edges are classified made for fairly straightforward translation from my highly sophisticated hand drawn network to the world of excel and ultimately Gephi. The resulting graph is immediately understood to be more complicated than the one that I drew because it creates edges for both sides of a conversation of dialogue rather than using a single edge to indicate contact between characters. This makes for a curvy picture reminiscent of something drawn by a child on Microsoft paint rather than the skeletal geometric proof that shares the same data. The greatest advantage to using the Gephi graph is perhaps its use of shading for the edges to indicate the weight of each connection. Though these data could also be found by looking at the number of edges and their respective sources/targets, the visual representation within the actual network allows for easier access to this information and the ability to compare the weight of these connections with other relationships within the text.

All of that being said, I’m struggling to think of something that I learned from this process that I didn’t already learn from creating my masterful network of orange and silver dots. I did find that the Gephi graph was able to illustrate more information at a single glance than my hand-drawn network, and I am inclined to think that the greatest advantages of using this tool may be more pedagogical rather than research based. With Gephi, we finally have a standardized(ish) method to create visual representations of interactions and relationships contained within a specific text. This can create an environment that allows readers who are either visual learners, or perhaps those who can be tripped up by challenging language within a text (i.e. Shakespeare) to appreciate aspects of literature that may have been more difficult for them to learn or teach in the past. This change in scale can be a great option for teaching a single text, and should be investigated further to provide a framework for teaching anything from Hamlet to War and Peace.