An exciting pattern emerging from graph theoretic studies is that discoveries made in specific networks often generalize to other networks. For example, Watts and Strogatz first described the small-world architecture (discussed below) in the U.S. power grid, the C. elegans neural network, and a network of film actors (Watts and Strogatz), but this property has now been reported in hundreds, if not thousands, of other datasets. Such similarities suggest that complex networks may be governed by fundamental, knowable principles, and that discoveries made in one field may well apply to networks in other fields. This generalizability strongly suggests that neuroscientists studying the brain should be interested in the properties of other real-world and model networks. Accordingly, rs-fcMRI studies can leverage a substantial graph theoretic literature to explore the properties of functional brain networks, such as which nodes are critical for information flow or network integrity, which parts of functional networks are better structured for local versus global processes, and what types of processes could give rise to observed network structures. Recent graph theoretic studies have begun to explore such issues
