To address the growing availability of complex network data, [3] introduced partially exchangeable stochastic block models for multilayer networks using random partition priors based on hierarchical normalized completely random measures. With this approach, the layer division information carried by a node-colored multilayer network is induced by imposing the suitable distributional invariance to the prior, leading to a new and probabilistically coherent way of modeling complex networks. In this paper we leverage these models to analyze multiple node-colored networks.