Résumé:
With the rise of social networks, the task of community detection in networks has become
increasingly challenging in recent years.
In order to detect communities, numerous algorithms have been proposed to identify dis-
joint communities. The major challenge in real-world community detection is determining
stable communities. Overlapping nodes belonging to multiple communities are therefore
difficult to detect. In this thesis, we have developed a new community detection method
based on density, where our method forms clusters through iterations using a specific
similarity criterion.
Our approach stands out for its efficiency, simplicity, and ease of implementation. We
compared our algorithm to several state-of-the-art algorithms using real networks, eva-
luating the results using the modularity measure Q. The results we obtained are considered
acceptable.
