Community structure is relevant to understand the properties of social networks and predict their behavior. But when this study includes the dynamic evolution, finding these communities and following them through time can be even more useful: it may help us to understand how social networks grow and to develop constructive models. In this article we analyze a dynamic blog dataset with a static community detection algorithm based on modularity, and then we use a similarity measure in order to follow the communities through time. Finally we develop a tool to visualize the dynamics of the network. This tool provides a fast intuition about the evolution of the community structure.