01236nas a2200121   4500008004100000245004600041210004400087260001300131520084400144100002300988700002101011856008201032       2019                        eng d00aEva: Attribute-Aware Network Segmentation0 aEva AttributeAware Network Segmentation  bSpringer3 aIdentifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/eva-attribute-aware-network-segmentation