Community detection has recently received increased attention due to its wide range of applications in many fields. While at first most techniques were focused on discovering communities in static networks, lately the focus has shifted toward evolving networks because of their high relevance in real-life problems. Given the increasing number of the methods being proposed, this paper explores the current availability of empirical comparative studies of dynamic methods and also provides its own qualitative and quantitative comparison with the aim of gaining more insight in the performance of available methods. The results show that no single best performing community detection technique exists, but rather, the choice of the method depends on the objective and dataset characteristics.