The study of networks is an established and rapidly evolving multidisciplinary field, spearheaded by a branch of mathematics called graph theory (of note, networks are also called graphs). In graph theory terms, a network is a collection of items (called nodes or vertices) that possess pairwise relationships (called edges). Over the last 15 years, the increased availability of large, high-quality datasets has fundamentally changed how networks are understood and modeled. To name one example, in 1999 Barabasi and colleagues reported the presence of a surprisingly large number of highly connected nodes (called hubs) in the World Wide Web (Albert et al., 1999). This finding was at odds with classical models of network structure and growth, and spurred interest in “preferential attachment” models of network growth (Barabasi and Albert, 1999), in which nodes joining a network preferentially attach to well-connected nodes, rather than randomly connecting to the network. Preferential attachment makes intuitive sense in many situations (consider the first-publisher advantage in citation networks (de Solla Price, 1965; Newman, 2009)), and indeed variants of this model appears to generally capture the behavior
