Lots of things can be represented as graphs. This is particularly true in this day of Big Data, online social networks, and the Internet of Things. In particular, online social networks are big business, with sites such as Facebook that have over 500 million active users (50 percent of them log in each day). These sites often monetize themselves by targeted advertising. However, for users to be engaged with a website, they often need to follow interesting people or pages.

In this chapter, we will look at the concept of similarity and how we can create graphs based on it. We will also see how to split this graph up into meaningful subgraphs using connected components. This simple algorithm introduces the concept of cluster analysis—splitting a dataset into subsets based on similarity. We will investigate cluster analysis in more depth in Chapter 10, Clustering News Articles.

The topics covered in this chapter include: