Structure matters in cognitive science. Whether we are asking about memory retrieval, semantic representations, categorization, language acquisition, learning from complex information, aging, or creativity, cognitive scientists often find themselves forced to reckon with structure. Network science offers a quantitative approach for doing this by allowing us to ask questions about the relationships between various entities at scales ranging from dyads, to communities, to entire systems. In this case, the entities are the nodes in the network and the relationships are the edges between them. Exploring how this plays out in actual practice is incredibly varied, aesthetically and intellectually beautiful, and deeply rewarding, allowing us to develop and test hypotheses about cognition that are not otherwise possible. As a metric ruler measures length, allowing us to compare human height with the Burj Khalifa, network science measures structure, allowing us to compare the structure of our environments with the structure of our cognitive representations, how those representations change across the lifespan, and how different processes interacting with those structures generate behavior.