Community detection in social networks is a widely studied topic in Artificial Intelligence and graph analysis. It can be useful to discover hidden relations between users, the target audience in digital marketing, and the recommender system, amongst others. In this context, some of the existing proposals for finding communities in networks are agglomerative methods. These methods used similarities or link prediction between nodes to discover the communities in graphs. The different similarity metrics used in these proposals focused mainly on common neighbors between similar nodes. However, such definitions are missing in the sense that they do not take into account the connection between common neighbors. In this paper, we propose a new similarity measure, named