Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting EVA, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate EVA on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that EVA is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity. We also investigate two well-defined applicative scenarios to characterize better EVA: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and ii) the node label prediction task, namely the problem of inferring the missing label of a node.