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.

Here we explore the mechanisms and drivers behind the impact disparity discussed in the previous chapter, focusing on what factors create high-impact papers and what conditions contribute to the lognormal distribution citations follow. We show how a rich-get-richer phenomenon similar to preferential attachement, growth, and fitness all contribute to the impact of a paper. We describe a fitness model that can effectively represent these dynamics, providing insight into how impact is created in science.