Compared to other digital tools such as text mining and data mapping, network analysis tools provide a more specialized way of visualizing data. As Ruth and Sebastian Ahnert explain in The Network Turn, one possible description of a network approach is that it is a way “to see features in history, language, literature, or art that would otherwise remain inscrutable;” in essence, a network highlights all possible connections, links, and other shared data that can convey broader patterns or themes. Gephi provides numerous different network graph tools, such as the standard bimodal graph:

This graph shows connections between prominent actors in early twentieth-century African American films; by viewing each node’s links, users can see which actors or movies are more prominent or what kinds of communities formed those who interacted often. The example of the Linked Jazz project demonstrates a similar approach to a network graph by showing each node with all of its potential connections, illustrating which American jazz artists knew of or interacted with each other. From there, further research can be done on what those connections mean or why they came about. Questions about specific clusters can be answered by a different approach to the network graph:

This version clearly delineates color-coded clusters within the network where certain nodes are more closely connected than with other parts of the graph. By reviewing the information specific to those nodes in the clusters, users can potentially learn how those communities were formed and why they were kept together. The Tudor Networks project demonstrates this approach by clustering nodes, representing correspondence, for each individual with specific groups or people. Each incoming or outgoing letter between people reveals factors about their relationships, which provides opportunities for further research on the nature of that communication and how those separate peoples and groups interacted. The unimodal model of network graph showcases a similar visualization of connections between groups:

By viewing these network graphs, researchers can learn about the nature of specific communications between people, groups, and communities, which allows for many wider questions on relevant social trends, issues, and problems that drive such interaction. Overall, network graphing is a specialized form of data visualization that provides valuable insight into the connections and internal interactions of a dataset.