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

Key User Extraction Based on Telecommunication Data (aka. Key Users in Social Network. How to find them?)