One doesn't have to look far to recognize the enormous growth of network graphs as a means to explore and explain networks. Social Network Analysis (SNA) has certainly been the most visible subset of network graph analysis, with thousands of cases where users have mapped Facebook, Twitter, and LinkedIn peer networks. While this has been, and continues to be, a viable use of the approach, there are many lesser known, but frequently more compelling, datasets with highly interesting networks that are available for our exploration. In the next few sections, we will walk through some of the primary categories where we can access data and use Gephi to construct highly informative graphs by employing definitions laid out in the book Networks, Crowds, and Markets: Reasoning about a Highly Connected World, by David Easley and Jon Kleinberg.
Collaboration graphs represent one of the more frequently encountered categories in the world of network analysis. These graphs include networks where individual nodes are connected based on having some sort of collaborative relationship. The nodes might represent individuals or institutions; these graphs often depict collaborative research between universities and their staff within a specific discipline.
Another popular utilization of network analysis has been through the viewing of network graphs based on a variety of communication methods between the actors in a network, often within the confines of a single corporation, organization, or educational institution. These graphs can be constructed using e-mail or phone communications, and will often focus on the frequency of contact, thus exposing the true information flows and power structures within the organization.
Graphs that examine the flow of information across the Web are typical of this category of network analysis. These linkages can reference anything from connections between bloggers, pages on Wikipedia, or among scientific paper citation networks. This is a very popular type of graph, given the accessibility of information via the Web and its various applications.
This category often manifests itself through physical structures but, as David Easley and Jon Kleinberg note, there are underlying economic structures here as well, in the form of companies, regulatory bodies, and other organizations. In these sorts of networks, connections between nodes are likely to refer to a literal physical linkage, as in connections between routers or computers.
Another discipline that has received much attention through the use of networks is in the world of biology, where graphs are used to show relationships between predators and prey, neural networks within the brain, and a number of other science-based scenarios. Other network types are likely to emerge as well, and hybrids of these networks is also a possibility.
The idea here is to help stimulate thought processes about what sort of graphs you might be most interested in creating, and begin working toward their creation using Gephi. Specific examples of each of these network types are provided in the Appendix, Data Sources and Other Web Resources.