This paper introduces key centrality measures in social networks and proposes a new perspective using relational algebra. All measures are implemented in the programs CENTRAL and FLOWIND. The paper discusses mathematical preliminaries, including relations, their properties, and operations. It then presents node centrality measures, including degree, closeness, and betweenness, as well as flow measures. It also covers network centralization measures, which assess the extent to which a network is dominated by a single node. The paper discusses algorithmic aspects, including methods for computing transitive closure and eigenvalues. An example is provided using student government data, where various centrality indices are calculated. The paper concludes with references to related works.This paper introduces key centrality measures in social networks and proposes a new perspective using relational algebra. All measures are implemented in the programs CENTRAL and FLOWIND. The paper discusses mathematical preliminaries, including relations, their properties, and operations. It then presents node centrality measures, including degree, closeness, and betweenness, as well as flow measures. It also covers network centralization measures, which assess the extent to which a network is dominated by a single node. The paper discusses algorithmic aspects, including methods for computing transitive closure and eigenvalues. An example is provided using student government data, where various centrality indices are calculated. The paper concludes with references to related works.