We consider data mining methods on large graphs where a set of labels is associated to each vertex. A typical example concerns the social network of collaborating researchers where additional information concern the main publication targets (preferred conferences or journals) for each author. We investigate the extraction of sets of dense subgraphs such that the vertices in all subgraphs of a set share a large enough set of labels. As a first step, we consider here the special case of dense subgraphs that are cliques. We proposed a method to compute all maximal homogeneous clique sets that satisfy user-defined constraints on the number of separated cliques, on the size of the cliques, and on the number of labels shared by all the vertices. The empirical validation illustrates the scalability of our approach and it provides experimental feedback on two real datasets, more precisely an annotated social network derived from the DBLP database and an enriched biological network concerning protein-protein interactions. In both cases, we discuss the relevancy of extracted patterns thanks to available domain knowledge.