  Description of temporal networks and detection of dynamic communities have
been hot topics of research for the last decade. However, no consensual answers
to these challenges have been found due to the complexity of the task. Static
communities are not well defined objects, and adding a temporal dimension
renders the description even more difficult. In this article, we propose a
coherent temporal clustering method: the Best Combination of Local Communities
(BCLC). Our method aims at finding a good balance between two conflicting
objectives : closely following the short time evolution by finding optimal
partitions at each time step and temporal smoothness, which privileges
historical continuity.
