netrankr: An R package for total, partial, and probabilistic rankings in networks

David Schoch



One of the key concepts in network science is network centrality. Centrality seeks to provide the answer to the question of who (or what) is important in a network depending on the underlying process forming the network and the empirical phenomenon in question. In a nutshell, an actor in a network is more central if they have better relations, where the definition of better relations depends on the conceptualization of structural importance. Applications of centrality can be found in any field where networks arise. In social networks, we may simply be interested in finding the most popular user. In bioinformatics, centrality is used to detect essential proteins in protein-protein interaction networks (Jeong et al., 2001). Even in sports, centrality is applied to rank athletes or teams (Radicchi, 2011). A myriad of indices have been proposed, all with differing interpretations of what constitutes a central position within a network. Although netrankr offers this traditional approach to network centrality, its main focus lies on alternative assessments of centrality based on partial and probabilistic rankings in networks.

Journal of Open Source Software 7 (77), 4563