基于电信数据的关键用户提取。社交网络中的关键用户。如何找到他们？）
Key User Extraction Based on Telecommunication Data (aka. Key Users in Social Network. How to find them?)
摘要：
在过去的几年里，收集大量用户数据的系统数量迅速增长。这些系统中的许多不仅包含关于人的特征的数据，而且包含关于他们与其他系统用户的关系的数据。从这种数据中，可以提取反映系统用户之间联系的社交网络。此外，这种社会网络的分析，使调查不同的特点，其成员和他们的联系。对这种网络的检查类型之一是关键用户提取。关键用户是指对其他网络成员影响最大的用户，也是对网络演进有重要影响的用户。所获得的关于这些用户的知识使得能够调查和预测网络内的变化。因此，这些知识对于从电信公司等网络中获利的人或公司来说非常重要。第二件重要的事情是能够提取这些用户尽可能快，即开发的算法，将是时间有效的大型社交网络的节点和边缘的数量等于几百万。本文主要分析了关键用户的提取方法，即社会地位。此外，社会地位的措施进行了比较，这是用来评估一个节点的中心性与其他方法。此外，还介绍了三种用于社会位置计算的算法，并将它们的处理时间与其他中心性方法进行了比较。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
