As Derek de Solla Price famously noted in 1965, the scientific literature forms a vast network (Price, 1965). The nodes of this network are the millions of published articles, and the edges are the citations between them. There is a wealth of information—not only within the content of these nodes (the text), but also within the structure connecting these nodes (the network topology). In fact, the network topology by itself provides clues about the quality of the content. This is similar to how Google’s PageRank algorithm harnesses the hyperlink structure of the web to evaluate web pages (Page, Brin, Motwani, & Winograd, 1998).

Surprisingly, scholarly evaluation over the last century has largely ignored this network property. The well-known Impact Factor simply counts the number of incoming links; it does not take into account the source of a citation and therefore ignores the extra information in the network (Garfield, 1955). So, why has it taken decades for network methods to become standard in the field of bibliometrics? Though researchers have long recognized the potential of a network approach, the field has suffered from a lack of computational resources and data. However, in this data-driven age, citation networks are now a staple of bibliometrics and are used as model systems in other disciplines.