A graph is a collection of elements, usually called nodes, linked together by edges (sometimes called branches). It is a common structure for mapping connections of many related elements. This is partly because the visual representation of a network shows the sort of connectedness that makes sense to someone familiar with the data, whether as a free-form map of associations written out on paper (sometimes called a mind map) or, in computer science, as a visual analogue to a common data model for connections between many elements.
Graphs are very popular nowadays, but they're often not as informative as other more specific ways to represent the connections between items of data. This chapter shows a graph that works for the data in question and another that doesn't. This will help us explore the strengths and weaknesses of graphs and learn how to make them useful.
Along the way, we'll examine the general problem of dealing with quantities of data too large to show meaningfully. We'll use a lot of mining, filtering, and interaction to bring out the meaning in a large data set.
This chapter also takes you out of the comfortable but limited Processing IDE used in previous chapters, and shows you how to integrate the Processing libraries with Eclipse or another Java environment of your choice.
Before getting too deep into the theory of graphs, let's start with a simple example that will help illustrate some of the successes and difficulties of representing ...