  The number of systems that collect vast amount of data about users rapidly
grow during last few years. Many of these systems contain data not only about
people characteristics but also about their relationships with other system
users. From this kind of data it is possible to extract a social network that
reflects the connections between system's users. Moreover, the analysis of such
social network enables to investigate different characteristics of its members
and their linkages. One of the types of examining such network is key users
extraction. Key users are these who have the biggest impact on other network
members as well as have big influence on network evolution. The obtained about
these users knowledge enables to investigate and predict changes within the
network. So this knowledge is very important for the people or companies who
make a profit from the network like telecommunication company. The second
important thing is the ability to extract these users as quick as possible,
i.e. developed the algorithm that will be time-effective in large social
networks where number of nodes and edges equal few millions. In this master
thesis the method of key user extraction, which is called social position, was
analyzed. Moreover, social position measure was compared with other methods,
which are used to assess the centrality of a node. Furthermore, three
algorithms used to social position calculation was introduced along with
results of comparison between their processing time and others centrality
methods.
