  One of the most interesting topics in social network science are social
groups. Their extraction, dynamics and evolution. One year ago the method for
group evolution discovery (GED) was introduced. The GED method during
extraction process takes into account both the group members quality and
quantity. The quality is reflected by user importance measure. In this paper
the influence of different user importance measures on the results of the GED
method is examined and presented. The results indicate that using global
measures like social position (page rank) allows to achieve more precise
results than using local measures like degree centrality or no measure at all.
