  Social relationships can be divided into different classes based on the
regularity with which they occur and the similarity among them. Thus, rare and
somewhat similar relationships are random and cause noise in a social network,
thus hiding the actual structure of the network and preventing an accurate
analysis of it. In this context, in this paper we propose a process to handle
social network data that exploits temporal features to improve the detection of
communities by existing algorithms. By removing random interactions, we observe
that social networks converge to a topology with more purely social
relationships and more modular communities.
