  Identifying 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
