Adding to the various software that we have worked with throughout this unit is Gephi, which allows the user to create network analysis graphs. Again, after viewing the example projects of Viral Texts, the Tudor Papers, and Linked Jazz, I thought I would be in way over my head. I especially felt this after viewing the Tudor Papers and the sheer number of paths that were charted with the letters and people that sent them. After I viewed the Linked Jazz project, I felt a bit better. So, when I began working on the data from the African American Films from 1909 to 1930, I better understood what I was getting myself into. Uploading the data was very simple after installing Gephi.  The first method of evaluating the data from the films was viewing the connections between the movies and actors. The below bimodal graph highlights this.

Not only can you customize the data’s color, background, and theme, but each point links you to the films the actor was in. Beyond this, you can also see the average distribution of the number of movies the actors participated in. This provides a better understanding of what the average number of films that the actors were in.

After working with the first data analysis, the second one showed the communities and connections between actors and specific films. Not only does it create groups based on shared projects, but you can also single out particular groups and evaluate the group members even closer. This fosters greater connections throughout the data. The below graph illustrates this.

Finally, when working with the data, you can expand the communities into an unimodal graph that charts all of the connections between the groups. After looking at the graph below, one begins to understand the vast network of the African American film industry in the 1900s.

Working with network analysis creates connections with the dataset and shows the broader picture of what that data is trying to tell the scholar. Gephi, in particular, was not only a relatively easy software to work with, but its vast customization and varying types of models create a wide array of options to represent one’s data.  It allows for scholars to answer basic questions such as: how are the various datasets connected? What are the patterns throughout the data? Are their specific data points more connected to others in the dataset? What is the overall significance of this data? This unit’s modules showed me the importance of data creation and representation and how much can be done with software relatively easily. Gephi unlocks the potential for researchers to create much more from their work.