As we noted earlier, Gephi provides an array of secondary tabs that surround the main workspace, permitting the user to execute actions on the graph without the need to toggle between multiple windows. With this approach, the impact of filters, partitions, color and size adjustments, and much more can be seen instantly, making it easy for the user to take an iterative approach to manage and analyze the graph.
The next several sections will provide an overview of how to use each of these tabs, without going into greater detail at this point. Some of these options, such as filtering and statistics, will be covered in much greater detail later in the book, while further information on other functions can always be found on the Gephi wiki or in the user forums.
The filtering tab is where we will eventually examine our graph output using a range of criteria, so that we gain a better understanding of our network. Many times, our network will be very large, dense, and thus difficult to navigate in its entirety. In these cases, filters provide us with the necessary tools to begin probing the graph systematically, searching for specific attributes or graph features. Note that Gephi gives us the ability to create individual or compound filters, where multiple conditions are nested.
The application of filters in Gephi is not always easy, so we will devote an entire chapter (Chapter 5, Working with Filters) of the book to them, as they can and should become a very powerful component of our Gephi skillset.
Not all graphs are created with the end goal of analysis or measurement, but for those who wish to understand network interactions and patterns, the statistics tab can provide a wealth of information. Gephi provides an array of statistical graph measures that can be employed to better understand the structure of a network, and ultimately can be used to compare networks to one another.
Chief among these statistics are a variety of centrality measures to be applied at the node level, including betweenness centrality, eigenvector centrality, closeness centrality, and eccentricity. Other measures include graph diameter, clustering coefficients, edge betweenness, and average degree measures. Many of these are included with the base Gephi installation, while others are available through selected plugins. Chapter 6, Graph Statistics, will be devoted to discuss these statistics using individual graph examples to drive a greater understanding on how to measure the network, and what the numbers mean.
The selection of an appropriate layout can make the difference between creating an impenetrable graph that fails to communicate a story versus an easily accessible visualization that not only communicates, but also has aesthetic appeal. In perusing the network graph literature, one is bound to come across the term hairball, a description for a very dense network with many connections that is all but undecipherable using standard graph algorithms.
By using Gephi, we have the ability to test many layout algorithms before settling on a final choice. This provides us with an opportunity to not only avoid the hairball issue, but also to find a layout that is most complimentary to the underlying network. Many of the layout algorithms provide options that allow the user to determine the ideal spacing within a graph by tinkering with attraction, repulsion, gravity, and other available settings.
We can also determine whether to employ a force-directed graph that displays a network based on the aforementioned attraction and repulsion settings, or to select a predetermined layout that arranges the network in a circle or set of concentric circles ordered by some sort of categorical ranking. In other words, Gephi makes it possible to explore network data using a wide variety of layouts, making it possible for us, the users, to select the best possible option for our graph.
We will explore many of these layout options in greater detail in Chapter 4, Network Patterns, by comparing and contrasting outputs using a variety of methods.