 The second strategy employs computational tools such as cluster analysis to find collections of nodes that are especially highly interconnected. To interpret these clusters in terms of mechanisms operative in the cell, researchers often align them with Gene Ontology (GO), a resource that was developed to represent published information about cell components, molecular functions, and biological processes in directed acyclic graphs so as to facilitate communication between researchers working on different species (Ashburner et al. 2000). Leonelli (2016) examines many of the epistemic issues the developers of GO confronted in developing such a resource. Beyond its original function, GO is now widely used to interpret network graphs mechanistically. By annotating nodes in cellular networks with information from GO when it is available researchers interpret these clusters as corresponding to known mechanisms or sometimes as constituting previously unknown mechanisms. Researchers make inferences about nodes for which such information is lacking using principles such as guilt by association—the entity is inferred to occur in the same location or to contribute to the same process as those with which it clusters (Bechtel 2017 analyzes several examples).