The community detection in complex networks has become a major field of research. Disjoint community detection deals often with getting a partition of nodes where every node belongs to only one community. However, in social networks, individuals may belong to more than one community such as in co-purchasing field, a co-authorship of scientist papers or anthropological networks. We propose in this paper a method to find overlapping communities from pre-computed disjoint communities obtained by using the core detection label propagation. The algorithm selects candidates nodes for overlapping and uses belonging functions to decide the assignment or not of a candidate node to each of its neighbours communities. we propose and experiment in this paper several belonging functions, all based on the topology of the communities. These belonging functions are either based on global measures which are the density and the clustering coefficient or on average node measures which are the betweenness and the closeness centralities. We expose then a new similarity measure between two covers regarding the overlapping nodes. The goal is to assess the similarity between two covers that overlap several communities. We finally propose a comparative analysis with the literature algorithms.