classification of social networks, social network model, social network graph, social network analysis method, target audience, psychological influence, information operation


The information war waged by the enemy against Ukraine is no less dangerous than direct combat operations on the front line. Considering the experience of the Russian Federation's large-scale armed aggression against Ukraine, we can conclude that the enemy is trying to undermine the unity of Ukrainian society, citizens' trust in the authorities and the armed forces. Social networks are a modern and powerful tool for the distribution of special information materials for psychological influence on the enemy. Methods of analysis and models of social networks are of interest to scientists when conducting research within the framework of combat (special) tasks. Analysis of information in social networks about the behavior, personal information, opinions and views of agents of social networks is necessary when conducting information operations. For the analysis of data in social networks, there are many applications, which are used to model information flows, processes of interaction of agents in the network, predict their behavior, calculate parameters and visualize the network graph. Using an information technology system or specialized software, it is possible to manage a large number of accounts through a group administrator and influence the behavior of other agents. In order to increase the effectiveness of the psychological influence of agents of social networks on target audiences, it is necessary to develop models of social networks to study the patterns of distribution of special information and establish connections and interaction of agents with the target audience of the enemy. In this article, the classification of social network analysis methods is carried out, the main indicators characterizing social networks are described, and the models of social networks are considered. To visualize the obtained results, regarding the classification of methods and models, structural diagrams have been developed and presented. The prospect of further research is the development of graph neural networks, which will allow modeling the interactions and properties of graphs to assess the level of psychological influence in the interests of information operations. This model can use methods of graph convolutions (graph convolutions), which are based on local operators to analyze the network structure.


Tantipathananandh C., Berger-Wolf T., Kempe D. A Framework for Community Identification in Dy-namic Social Networks. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Dis-covery and Data Mining. N. Y.: ACM Press, 2007. pp. 717-726.

Sun J., Faloutsos C., Papadimitriou S., Yu P. Graphscope: Parameter-Free Mining of Large Time-Evolving Graphs. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Dis-covery and Data Mining. N. Y., 2007. pp. 687-696.

Ferlez J. Faloutsos C., Leskovec J., Mladenic D., Grobelnik M. Monitoring Network Evolution Using MDL. Proceedings of the International Conference on Data Engineering. 2008. pp. 1328-1330.

Berlingerio M., Bonchi F., Bringmann B., Gionis A. Graph Evolution Rules. Proceedings of the European Conference on Machine Learning and Knowledge Dis-covery in Databases. Lecture Notes in Computer Sci-ence. Springer, 2009. Vol. 5781. pp. 115-130.

Desikan P., Srivastava J. Mining Temporally Changing Web Usage Graphs. Proceedings of the Inter-national Workshop on Mining Web Data for Discover-ing Usage Patterns and Profiles. 2004. pp. 1-17.

Inokuchi A., Washio T. A Fast Method to Mine Frequent Subsequences from Graph Sequence Data. Pro-ceedings of the IEEE International Conference on Data Mining. 2008. pp. 303-312.

Liu Z. Yu J., Ke Y., Lin X. Spotting Significant Changing Subgraphs in Evolving Graphs. Chen Pro-ceedings of the 8th International Conference on Data Mining. 2008. pp. 917-922.

Borgwardt K., Kriegel H., Wackersreuther P. Pattern Mining in Frequent Dynamic Subgraphs. Pro-ceedings of the IEEE International Conference on Data Mining. 2006. pp. 818-822.

Borgatti S. Analyzing Social Networks. Redes Revista hispana para el análisis de redes sociales. 2016. Vol. 27 (2). pp. 141-145.

Базарний С.В. Удосконалена математична модель психологічного впливу на агентів соціальних мереж в інтересах інформаційної операції. Труди університету: наук. журн. Нац.ун-т оборони України. Київ. 2023. №4(79)/2023. C.94-104., інв. №49882.

Базарний С.В. Метод виявлення агентів со-ціальних мереж, що мають найбільший вплив. Су-часні інформаційні технології у сфері безпеки та оборони: наук. журн. Нац.ун-т оборони України. Київ. 2023. №1(46)/2023. C.145-150.

Milgram S. The Small World Problem. Psy-chology Today. 1967. Vol. 2. pp. 60-67.

Jensen D., Neville J. Data Mining in Social Networks. Proceedings of the National Academy of Sci-ences Symposium on Dynamic Social Network Analysis. 2002. pp. 289-302.

Johnson J., Ironsmith M. Assessing Children's Sociometric Status: Issues and the Application of Social Network Analysis. Journal of Group Psychotherapy, Psychodrama & Sociometry. 1994. Vol. 47. Is. 1. pp. 36-49.

Koren Y. On Spectral Graph Drawing. Pro-ceedings of the 9th International Computing and Com-binatorics Conference. Springer, 2003. pp. 496-508.

Fortunato S. Community Detection in Graphs. Phys. Rep. 2010. Vol.486. No. 3–5. pp. 75-174.

Barabási А. Network Science Northeastern University, Boston July 2016. pp.127-133.





Cybersecurity & Critical Information Infrastructure Protection (CIIP)