The continuous interest in the social network area contributes to the fast development of this field. New possibilities of obtaining and storing data allows for more and more deeper analysis of the network in general, as well as groups and individuals within it. Especially interesting is studying the dynamics of changes in social groups over time. Having such knowledge ones may attempt to predict the future of the group, and then manage it properly in order to achieve presumed goals. Such ability would be a powerful tool in the hands of human resource managers, personnel recruitment, marketing, etc. The thesis presents a new method for exploring the evolution of social groups, called Group Evolution Discovery (GED). Next, the results of its use are provided together with comparison to two other algorithms in terms of accuracy, execution time, flexibility and ease of implementation. Moreover, the method was evaluated with various measures of user importance within a group. Obtained results suggest that GED is the best method for analyzing social group dynamics.