Network data often exhibit block structures characterized by clusters of\nnodes with similar patterns of edge formation. When such relational data are\ncomplemented by additional information on exogenous node partitions, these\nsources of knowledge are typically included in the model to supervise the\ncluster assignment mechanism or to improve inference on edge probabilities.\nAlthough these solutions are routinely implemented, there is a lack of formal\napproaches to test if a given external node partition is in line with the\nendogenous clustering structure encoding stochastic equivalence patterns among\nthe nodes in the network. To fill this gap, we develop a formal Bayesian\ntesting procedure which relies on the calculation of the Bayes factor between a\nstochastic block model with known grouping structure defined by the exogenous\nnode partition and an infinite relational model that allows the endogenous\nclustering configurations to be unknown, random and fully revealed by the\nblock-connectivity patterns in the network. A simple Markov chain Monte Carlo\nmethod for computing the Bayes factor and quantifying uncertainty in the\nendogenous groups is proposed. This routine is evaluated in simulations and in\nan application to study exogenous equivalence structures in brain networks of\nAlzheimer's patients.\n