The way networks grow and change over time is called network evolution. Numerous off-the-shelf algorithms have been developed to study network evolution. These can give us insight into the way systems grow and change over time. However, what off-the-shelf algorithms often lack are knowledge of the behavioral details surrounding a specific problem. Here we will develop a simple case that we will revisit over the next few chapters: How do children learn words from exposure to a sea of language? One possibility is that the words children learn first influence the words they learn next. Another possibility is that the structure of language itself facilitates the learning of some words over others. Indeed, we know that adults speak differently to children in ways that facilitate language learning, with semantically informative words tending to appear more often around words that children learn earliest. This invites the question: To what extent does the semantic structure of language predict word learning? This chapter will provide a general framework for building and competing models against one another with a specific application to the network evolution of child vocabularies.