In reading about network theory, there are elements in which networks are fundamentally different from Digital History’s other methodological frameworks: textual analysis and mapping. Like textual analysis, there is rigorous mathematical theories theories behind the practice of analyzing networks. Unlike textual analysis, networked data is much more agnostic. As Scott Weingart says, anything can be networks, even though it shouldn’t be. Maps are similarly heterogeneous with their data, allowing a wide variety of stuff to take up space in a variety of ways. Yet there is less structure or fewer mathematical rules in maps than traditional graph theory’s logical tendency to connect components or behave in certain ways.
Given these similarities and differences, it’s no surprise that network theory can act as a kind of glue in the digital humanities. One of the more powerful ideas that I read about and am starting to understand more intricately is how textual analysis traits can combine with networks to provide additional insights or explorations. Specifically how topic models can form networks to connect documents in new ways.
Networks can provide a rigor to maps that can unlock how events occur and are related to one another. As an example, I’m thinking of ORBIS. In looking at a map of the Roman Empire, historians can imagine how the trade and travel networks interact with the physical challenges to these networks. Elijah Meeks and ORBIS actually provide a certainty that can be rigorously analyzed with other information about the Roman Empire to approach new insights about the use of space.
In combining networks with other techniques in digital history, scholars can provide additional insights to their sources. Networks make textual analysis more richly connected. Geographic networks are more thoroughly contextualized. When used properly, network theory can connect aspects of digital history. Even when initially focusing on other methodologies, researchers should not forget about the power of networks and their ability to answer historical questions.
Yet, I would pause in my enthusiasm for networks. Because they are mathematically determined and have strictly theorized relationships and behaviors, there is less agency for individuals within the systems. This is a critique of many aspects of the digital humanities and one which we will discuss with Johanna Drucker’s conception of data in Graphesis. David Easley and Jon Kleinberg discuss urban segregation in their book Networks, Crowds, and Markets and particularly how actively segregating neighborhoods follow a particular pattern of balancing called the Schelling model. Anyone who has seen Washington, DC’s demographics change over the past decade is aware of the forces this model describes. Yet, it is equally incorrect in describing school attendance patterns between 1954 and 1975. In this case, the Supreme Court in Brown v. Board mandated that segregated schooling was not constitutional. Later decisions required that schools maintain racially balanced schools. In this case, public schooling did not proceed under the Schelling model, and it’s an important caveat to remember in assessing the power of networks.