Research into the Mechanics of the Agent-Informational Clustering in a Social Network

The thesis is devoted to the study of the mechanics of agent-informational clusters in a social network on the example of user segmentation tasks taking into account an influence criteria. The main features of data generated by social networks (social big data) and metrics that characterize influential network nodes are considered. A review of community-building algorithms based on the theory of social networks, as well as clustering methods based on machine learning, are carried out. Metrics for assessing the quality of segmentation quality are presented. The analysis of technological tools is performed. The results of the application of methods selected on the basis of the performed analysis to a test dataset are shown in a separate paper. The limitations of the applicability of considered approaches and possible problems during the implementation of algorithms in the field of social network analysis are described. Evaluation of the effectiveness is executed with the help of SIR epidemic spread models. The work may be in interest to both scientific communities and professionals solving marketing problems.