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.