01426nas a2200133   4500008004100000245007500041210006900116300001100185490000600196520096700202100002301169700002101192856007901213       2020                        eng d00aIdentifying and exploiting homogeneous communities in labeled networks0 aIdentifying and exploiting homogeneous communities in labeled ne  a1–200 v53 aAttribute-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.1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00302-1