<p>Voting is a core element of social choice theory and a subarea of computational social choice. The goal is to rank (or rate) candidates according to voters’ preferences, and to eventually select the winner(s) of the election. In multiwinner voting, the same applies to sets of candidates and winning committee(s). The intuition is therefore quite similar to centrality in networks, where the goal is to rate (or rank) nodes—or groups of nodes—according to their structural positions. We establish correspondences between these two research fields by deriving preference rankings from network relations, and adapting single- and multiwinner voting rules to identify the most central (groups of) nodes. The transfer of reasonable and desirable properties and axioms from social choice theory to network science opens up the possibility to study novel aspects of centrality, and leads to the definition of voting-based centrality measures.</p>