• Sharif Guseynov Liepaja University (LV)
  • Aleksandrs Berežnojs ISMA University of Applied Sciences Ventspils University of Applied Sciences (LV)
  • Jekaterina Aleksejeva Liepaja University Riga Secondary School 34, Riga (LV)



influences degree, Nash Equilibrium, optimal control, social networks


This paper examines social networks, where each agent is characterized by some dynamic parameters, the dynamics of which is resulting from the influence of other agents having their own objective functions and limiting factors, as well as from control/governing body with its own objective function. In this paper, referring to the type of social networks described above, the following two interrelated problems are investigated: the problem of determining the degree of information influence on social networks; the problem of finding optimal control in social networks.



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Aleksejevs, R., Guseinovs, R., Medvedev, A.N., & Guseynov, Sh.E. (2017). On a Multi-criterion Problem of Planning Maritime Cargo Transportation. Journal of Traffic and Transportation Engineering, 4, 124-146.

Amati, V., Lomi, A., & Mira, A. (2018). Social Network Modeling. Annual Review of Statistics and Its Application, 5(1), 343-369.

Bellman, R. (1997). Introduction to Matrix Analysis. Philadelphia: SIAM Publishing.

Bukharin, S.N. & Malkov, S.Yu. (2010). On the Question of Mathematical Modelling of Information Interactions. Information Warfares, 2(14), 14-20.

Bukharin, S.N., Kovalev, V.I., & Malkov, S.Yu. (2009). On Formalization of Information Field Concept. Information Warfares, 4(12), 2-9.

Coello Coello, C.A., Lamont, G.B., & van Veldhuizen, D.A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. New York: Springer-Verlag.

Chernets, V., Bazlova, T., & Ivanova, E. (2010). Influence Through Social Networks. Moscow: FOKUS-MEDIA.

Chkhartishvili, A.G., Gubanov, D.A., and Novikov, D.A. (2019). Social Networks: Models of Information Influence, Control and Confrontation. Cham, Switzerland: Springer Nature.

Dmitriev, V.I. & Guseynov, Sh.E. (1995). On matching the resolving power and detailedness of inverse problems solution. Herald of the Moscow State University, Series 15: Computational Mathematics and Cybernetics, 1, 24-27.

Dutta, P.K. (1999). Strategies and Games: Theory and Practice. Boston: MIT Press.

Farris, P., Pfeifer, Ph.E., & Johnson, R.R. (2009). The Value of Networks. Charlottesville: Darden School of Business, University of Virginia, Darden Case No. UVA-M-0645.

Fedyanin, D.N. & Chkhartishvili, A.G. (2010). Оn a problem of information control in social networks. Large-Scale Systems Control, 31, 265-275.

Feller, W. (1968). Introduction to Probability Theory and its Applications, Vol I. New York: John Wiley & Sons.

Fleming, P.J., & Pashkevich, A.P. (1986). Application of Multiobjective Optimization to Compensator Design for SISO Control Systems. Electronics Letters, 22(5), 258-259.

Fowler, J.H. & Christakis, N.A. (2011). Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. New York: Little, Brown and Company.

Galam, S. (2012). Sociophysics: A Physicist's Modeling of Psycho-political Phenomena. New York: Springer-Verlag.

Gembicki, F.W. (1973). Vector Optimization for Control with Performance and Parameter Sensitivity Indices. Ph.D. Thesis, Department of System Engineering. Cleveland: Case Western Reserve University.

Gembicki, F.W., & Haimes, Y.Y. (1975). Approach to Performance and Sensitivity Multiobjective Optimization: The Goal Attainment Method. IEEE Transactions on Automatic Control, 29(6), 769-771.

Goyal, M., Karamchandani, N., Chatterjee, D., & Manjunath, D. (2019). Maintaining Ferment: On Opinion Control over Social Networks. Proceedings of IEEE 58th Conference on Decision and Control, 5217-5222.

Grabich, N. & Rusinowska, A. (2010). A Model of Influence in a Social Network. Theory and Decision, 69(1), 69-96.

Gubanov, D.A. (2020). Influence in Social Networks: Formalization Variants. Large-Scale Systems Control, 85, 51-71.

Gubanov, D.A., Novikov D.A., & Chkhartishvili, A.G. (2009). Social Media Influence Models. Control in Socio-Economic Systems, 7, 205-281.

Gubanov, D.A., Novikov D.A., & Chkhartishvili, A.G. (2010). Social Networks: Models of Information Influences, Control and Confrontations. Moscow: FizMatLit Publishing.

Hoede, C. & Bakker, R. (1982). A Theory of Decisional Power. Journal of Mathematical Sociology, 8, 309-322.

Jackson, M. (2008). Social and Economic Networks. Princeton: Princeton University.

Johnson, N., Turnbull, B., Maher, Th., & Reisslein, M. (2021). Semantically Modeling Cyber Influence Campaigns (CICs): Ontology Model and Case Studies. IEEE Access, 9, 9365-9382.

Keeney, R.L. & Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Trade. Cambridge: Cambridge University Press.

Kovarik, B. (2015). Revolutions in Communication: Media History from Gutenberg to the Digital Age. New York: Bloomsbury Academic Publisher.

Malkov, S.Yu. (2016). Social Organization and Historical Process: Possibilities of Mathematical Modelling. Moscow: LIBROKOM Publishing.

McKenney, D., & White, T. (2018). Towards Distribution-based Control of Social Networks. Computational Social Networks, 5, Article ID: 1236.

Nash, J. (1950). Equilibrium Points in n-person Games. Proceedings of the National Academy of Sciences, 36(1), 48-49.

Nash, J. (1951). Non-Cooperative Games. The Annals of Mathematics, 54(2), 286-295.

Nguyen, V.X., Xiao, G., Xu, X.-J., Wu, Q., & Xia, Ch.-Y. (2020). Dynamics of Opinion Formation under Majority Rules on Complex Social Networks. Scientific Reports, 10, Article ID: 456, 9 p.

Razis, G., Anagnostopoulos, I., & Zeadally, Sh. (2020). Modeling Influence with Semantics in Social Networks: a Survey. ACM Computing Surveys, 53(1), Article ID: 7, 61.

Reed, D.P. (1999). That Sneaky Exponential: Beyond Metcalfe's Law to the Power of Community Building. Retrieved from

Reed, D.P. (2001). The Law of the Pack. Harvard Business Review, 79(2), 23-4, 154. PMID: 11213694.

Rusinowska, A. & Swart, H. (2007). Generalizing and Modifying the Hoede-Bakker Index. Theory and Applications of Relational Structures as Knowledge Instruments. Notes in Artificial Intelligence, 2, 60-88.

Simeonov, S. (2006). Metcalfe's Law: More Misunderstood than Wrong? HighContrast: Innovation & venture capital in the post-broadband era. Retrieved from

Steuer, R.E. (1986). Multiple Criteria Optimization: Theory, Computation, and Application. New York: John Wiley & Sons.

Tikhonov, A.N. & Arsenin, V.Ya. (1977). Solutions of Ill-Posed Problems. New York: V.H.Winston & Sons Publishing.

Vasin, A.A. & Morozov, V.V. (2005). Game Theory and Models of Mathematical Economics. Moscow: Lomonosov Moscow State University Press.

Vegh, S. (2003). Classifying Forms of Online Activism: The Case of Cyberprotests Against the World Bank. In McCaughey, M., & Ayers, M.D. (Eds.), Cyberactivism: Online Activism in Theory and Practice, (72-73). New York: Routledge & CRC Press.

Volodenkov, S.V. (2015). Internet-Communications in the Global Space of Modern Political Governance. Moscow: Moscow State University Press.

Xiong, F. & Liu, Y. (2014). Opinion formation on social media: An empirical approach. Interdisciplinary Journal of Nonlinear Science, 24(1), Article ID: 013130, 8 p.

Zhilyakova, L.Yu. (2019). Modeling the Structure of MIMO-Agents and Their Interactions. In Kuznetsov, S., & Panov, A. (Eds.), Artifical Intelligence, (3-16), 1093. Cham: Springer.




How to Cite

Guseynov, S., Berežnojs, A., & Aleksejeva, J. (2021). DETERMINATION OF SUBJECTS’ SIGNIFICANCE RATE AND OPTIMAL INFORMATION CONTROL IN SOCIAL NETWORKS. SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference, 3, 254-272.