Understanding the processes that give rise to networks gives us a better grasp of why we see the networks we do, where we might expect to find them, and how we might expect them to change over time. One way to achieve this is to create simulated networks. Simulated networks allow us to build networks based on detailed principles. We can then ask how networks derived from these principles behave and, correspondingly, understand how our observed networks may be generated by similar principles. This chapter explores many generative algorithms, including random graphs, small world networks, preferential attachment and acquisition, fitness networks, configuration models, amongst many others.

Words, like biological species, are born and then, someday, they die. The half-life of a word is roughly 2,000 years, meaning that in that interval about half of all words are replaced with an unrelated (noncognate) word. Where do the new words come from? There are numerous dimensions along which new words could vary from old words, so it may not be easy to see how to enter this problem. However, extending our small worlds metaphor and the observation of clusters in language, we tell a simple story that mirrors biological theories about the origin of species. Language has urban centers with well-populated and well-connected meanings (like *food* and *red*). It also has rural fringes, where words live more isolated lives as hermits with limited connections to other words (like *twang* and *ohm*). Are new words more likely to be born in urban centers or in the rural fringes?