The number of systems that collect a large number of data about users grow rapidly 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. The knowledge obtained about these users 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, e.g. 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 book the method of key user extraction, called social position, was analysed. 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 other centrality measures.
