DATA PROCESSING AND ANALYSIS
D. I. Korovin, E. V. Romanova, S. R. Muminova, A. V. Osipov, E. S. Pleshakova, N.M. Mazutskiy, T.M. Gataullin, S.T. Gataullin "Graph Analytics for Digital Economy Tasks"
INTELLIGENCE SYSTEMS AND TECHNOLOGIES
MATHEMATICAL MODELING
MANAGEMENT AND DECISION MAKING
SOFTWARE ENGINEERING
D. I. Korovin, E. V. Romanova, S. R. Muminova, A. V. Osipov, E. S. Pleshakova, N.M. Mazutskiy, T.M. Gataullin, S.T. Gataullin "Graph Analytics for Digital Economy Tasks"
Abstract. 

The manuscript discusses theoretical and practical examples of the use of graph analytics to solve the priority tasks of the digital economy, as the first stage of the knowledge economy. First, authors outline a simulation model which can be used to track fluctuations in inflation. Since a rise in inflation can reflect the rise in prices of goods and services, and how consumers lose ground as their earnings buy fewer goods, predicting inflation – and its effects – can be of great importance. The model presented is based on the cognitive graph. The cognitive graph has 15 vertices which are factors impacting the economy. The bonds between the vertices are analyzed and the incidence matrix is formed. Next, unbalanced cycles whose length is more than 2 are recognized in the graph. It is these unbalanced cycles that typically cause inflation, and the detriment to the economy. This model makes possible the examination of 5 unbalanced cycles, and its use allows the government (decision-makers) to implement to control and limit inflation’s effects on the economy. The theoretical basis applications of cognitive graphs for the quantitative assessment of knowledge are also presented.

Keywords: 

digital economy, knowledge economy, inflation; simulation modeling; cognitive graph; graph analytics; consumer price index; key rate.

PP. 33-45.

DOI 10.14357/20718632230304
 
References

1. Kochkarov, R. Multicriteria Optimization Problem on Prefractal Graph. Mathematics 2022, 10, 930. https://doi.org/10.3390/math10060930
2. Kochkarov, R. Research of NP-Complete Problems in the Class of Prefractal Graphs. Mathematics 2021, 9, 2764. https://doi.org/10.3390/math9212764
3. Ledashcheva, T.; Pinaev, V. Cognitive analysis of the socio-ecological and economic system of the region for choosing a sustainable development strategy (on the example of oil-producing regions of Russia). E3S Web of Conferences 2020, 169, 02020.
4. Paley, A.G.; Pollack, G.A.; Konova, E.A.; Kalashnikova, N.V. Building models of economic systems using cognitive methods. In Proceedings of the 31st International Business Information Management Association Conference, IBIMA 2018: Innovation Management and Education Excellence through Vision 2020, Italy, 25-26 April, pp. 4062-4069.
5. Ginis, L.A.; Gorelova, G.V.; Kolodenkova, A.E. Cognitive and simulation modeling of regional economic system development. International Journal of Economics and Financial Issues 2016, 6(5), 97-103.
6. Ivanyuk, V. Economies. Formulating the concept of an investment strategy adaptable to changes in the market situation. Economies 2021, 9, 95. https://doi.org/10.3390/economies9030095.
7. Sudakov, V. Improving Air Transportation by Using the Fuzzy Origin–Destination Matrix. Mathematics 2021, 9, 1236. https://doi.org/10.3390/math9111236.
8. Krakhmalev, O.; Krakhmalev, N.; Gataullin, S.; Makarenko, I.; Nikitin, P.; Serdechnyy, D.; Liang, K.; Korchagin, S. Mathematics Model for 6-DOF Joints Manipulation Robots. Mathematics 2021, 9, 2828.
https://doi.org/10.3390/math9212828.
9. Andriyanov, N.A.; Dementiev, V.E.; Tashlinskiy, A.G. Detection of objects in the images: from likelihood relationships toward scalable and efficient neural networks. Computer Optics 2022, 46(1). DOI: 10.18287/2412-6179-CO-922.
10. Bushin, P.Y.; Zakharova V.N. Mathematical methods and models in economics. RITS HGAEP: Khabarovsk, Russian Federation, 2006.
11. Kamaev V.A. Cognitive modeling of socio-economic systems. IUNL VolGTU: Volgograd, Russian Federation, 2012.
12. Korchagin, S.; Pleshakova, E.; Alexandrova, I.; Dolgov, V.; Dogadina, E.; Serdechnyy, D.; Bublikov, K. Mathematical Modeling of Electrical Conductivity of Anisotropic Nanocomposite with Periodic Structure. Mathematics 2021, 9, 2948. https://doi.org/10.3390/math9222948.
13. Soloviev, V. Fintech Ecosystem in Russia. In Proceedings of the 2018 11th International Conference; Management of Large-Scale System Development, MLSD, Moscow, Russia, 1–3 October 2018.
https://doi.org/10.1109/mlsd.2018.8551808.
14. Abramova, N.A.; Avdeeva, Z.K. Cognitive analysis and management of application: problems of methodology, theory and practice. Problems of management 2008, 3, 85-87.
15. Avdeeva, Z.K.; Makarenko, L.P.; Maximov, V.P. Cognitive technologies of decision-making support in strategic management of situations. Information Technologies 2006, 2, 15-25.
16. Gorelko, G.P.; Korovin, D.I. Mathematical modeling of the dynamics of changes in the qualitative indicators of the socio-economic system using weighted digraphs. Proceedings of higher educational institutions. Series: Economics, finance and production management 2013, 4(18), 84-91.
17. Gorelko, G.P.; Korovin, D.I. Modeling the interactions of factors of the socio-economic system of Russia by the method of graph theory. Proceedings of higher educational institutions. Series: Economics, finance and production management 2013, 2(16), 100-106.
18. Korovin, D.I. On the use of cognitive graphs to analyze the qualitative characteristics of socio-economic processes: corruption in the university. Proceedings of higher educational institutions. Series: Economics, finance and production management 2020, 2(44), 67-72.
19. Krakhmalev, O.; Korchagin, S.; Pleshakova, E.; Nikitin, P.; Tsibizova, O.; Sycheva, I.; Liang, K.; Serdechnyy, D.; Gataullin, S.; Krakhmalev, N. Parallel Computational Algorithm for Object-Oriented Modeling of Manipulation Robots. Mathematics 2021, 9, 2886.
https://doi.org/10.3390/math9222886.
20. Luchko, O.N.; Marenko, V.A. Cognitive modeling as a decision support tool. Publishing House of the Siberian Branch of the Russian Academy of Sciences: Novosibirsk, Russian Federation, 2014.
21. Korchagin, S.A.; Gataullin, S.T.; Osipov, A.V.; Smirnov, M.V.; Suvorov, S.V.; Serdechnyi, D.V.; Bublikov, K.V. Development of an Optimal Algorithm for Detecting Damaged and Diseased Potato Tubers Moving along a Conveyor Belt Using Computer Vision Systems. Agronomy 2021, 11, 1980.
22. Osipov, A.; Filimonov, A.; Suvorov, S. Applying Machine Learning Techniques to Identify Damaged Potatoes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). In Proceedings of the LNCS, 20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021, Virtual, Online, 21-23 June 2021. DOI: 10.1007/978-3-030-87986-0_17.
23. Kuznetsov, O.P. Methods of formalization, analysis and decision-making in poorly structured situations based on fuzzy cognitive maps. In Scientific session of MEPhI. Collection of research papers in 17 volumes. MEPhI: Moscow, Russian Federation 2007; Volume 3, pp. 27-30.
24. Pavlov, A.N.; Sokolov, B.V. Decision-making in conditions of fuzzy information: textbook. GUAP: Saint-Petersburg, Russian Federation, 2006.
25. Pegat, A. Fuzzy modeling and control. BINOM Knowledge Laboratory: Moscow, Russian Federation, 2011.
26. Mazny, G.L.; Kursova, N.V. Sign graphs and digraphs and their application in modeling and analyzing complex problems in ecology, psychology, economics and politics. Geoinformatics 1997, 3, 8-17.
27. Ilchenko, A.N. Economic and mathematical methods. Finance and statistics: Moscow, Russian Federation, 2006.
28. Gorelko, G.P.; Korovin, D.I. The use of balanced graphs to describe the dynamics of economic processes in socio-economic systems. Ivanovo State Power Engineering University named after V.I. Lenin: Ivanovo, Russian Federation, 2013.
29. Roberts, F.S. Discrete mathematical models with application to social, biological and environmental problems. Nauka: Moscow, Russian Federation, 1986.
30. Goncharov, D.K. Development of hidden key competencies of the enterprise. Creative Economy 2009, 5, 92-96.
31. Luchko, O.N.; Marenko, V.A.; Khveckovich, E.B. Information and analytical systems. OmGA: Omsk, Russian Federation, 2010.
32. Marenko, V.A.; Luchko, O.N.; Stripping, L.O. Information and analytical work in socio-economic systems. Publishing house of SB RAS: Novosibirsk, Russian Federation, 2010.
 

2024 / 02
2024 / 01
2023 / 04
2023 / 03

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".