Centrality is an important concept in social network analysis which involves
identification of important or prominent actors. Three common definitions
of centrality are degree centrality, closeness centrality and betwenness
centrality. These definitions yield actor indices which can be aggregated
across actors to obtain a single group-level index. In this paper we consider
how eight of these group-level indices can be used for graph centrality
tests. Two of the tests are based on degree, whereas the remaining six
tests are based on closeness. Our null hypothesis model, showing no centrality
structure, is the Bernoulli graph model which we test against\ a block
model reflecting graph centrality. We perform a simulation study where
the power of the tests are compared.
Keywords: Bernoulli graphs; Closeness centrality; Degree centrality;
Power of centrality tests; Random graphs; Stochastic blockmodels.