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A method to identify expert users in social networks
Luneva Elena

PhD in Technical Science

Associate Professor, Department of Information Technology, National Research Tomsk Polytechnic University

634050, Russia, g. Tomsk, ul. Pr. Lenina, 30

Efremov Aleksandr

Senior Lecturer, Department of Automation and Robotics, National Research Tomsk Polytechnic University

634050, Russia, Tomskaya oblast', g. Tomsk, ul. Lenina, 30

Banokin Pavel

Assistant, Department of Information Technologies, National Research Tomsk Polytechnic University

634050, Russia, Tomskaya oblast', g. Tomsk, ul. Pr. Lenina, 30



The subject of the research is methods and approaches for solving a class of key player search tasks (key player problem) applicable for identifying expert users in a certain subject area on social networks; a model for building social graphs from data selected from a social network; methods for constructing weighted oriented random graphs for model experiments and their comparative analysis; methods of cluster analysis of the ranking results of social network users; comparative analysis of various results of the identification of expert users in a given subject area. The research methods used in this work are based on system analysis methods, cluster analysis tools, graph theory, and social network analysis methods. To assess the performance of the proposed method, model experiments were carried out using a computer and experiments on real data. In the process of software implementation of the service, the methods of the theory of algorithms, the theory of data structures, and object-oriented programming were used to demonstrate the operability of the method. A method has been developed for identifying expert users on social networks in a given subject area, taking into account the quantitative data on the activity of these users. Unlike existing methods, users of a social graph can be ranked using two or more effective methods, which allows them to take advantage of these methods, and the method itself provides an opportunity to obtain additional information about users who are influenced by expert leaders, as well as potential hidden leaders public opinion.

Keywords: affinity propagation, Kendall-Wei ranking, Borgatti measure, opinion leader, social network, cluster analysis, directed graph, key players, social graph, user identification



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