Numerous methods have been proposed in order to extract clusters from social networks. While many studies are conducted on the design of innovative methods that are able to search for various kinds of clusters, most approaches make the assumpion of static networks. One of the recent methods concerns the search for conceptual links. This is a new approach of link clustering that exploits both network structure and node attributes in order to identify frequent links between groups of nodes in which nodes share common attributes. In this work, we focus on the tracking of conceptual links in dynamic networks, namely networks experiencing important structural changes during their evolution. In particular, we seek to understand how conceptual links appear and evolve during the network development. For this purpose, we propose a set of measures that aim to capture some behaviors caracterising the evolution of these clusters. Our approach is thus used to understand the evolution of the conceptual links extracted on two real world networks: a scientific co-author network and a mobile communication network. The results obtained highlight significant trends in the evolution of the conceptual links in these two netorks.