So far, you've got some handy network formats in your toolbox. But, if your data is too complex or too messy to easily convert into one of the previous formats, you might have to build your network from scratch, adding edges and nodes one at a time. Luckily, the techniques you learned in Chapter 2, Working with Networks in NetworkX, are all you really need! This section walks through a practical example of building a network programmatically from a real data set.
The example in this section is a word co-occurrence network. These networks are used to understand the relationship between words in a particular set of documents. In a co-occurrence network, nodes represent words and edge weights represent how many documents they appear in together. Here, "document could mean any collection of words: blog post, paragraph, sentence, carefully arranged...
