Graph Methods for Describing the Trade Profile of a Region
https://doi.org/10.22394/1726-1139-2022-2-70-80
Abstract
The work is devoted to the actual problem of building the trade profile of the region and studying the sustainability of regional foreign trade relations by analyzing the results of the region’s foreign economic activity. The aim of the work is to develop a method for describing the trade profile of the region based on graph theory, as well as to assess the possibility of interpreting the behavior of the identified groups of goods from the point of view of analyzing the dynamics of import and export volumes. As the main hypothesis, the paper uses the statement that the network model of describing the foreign trade relations of the region should be balanced with respect to the expectations of suppliers and consumers of products, otherwise there is an imbalance that generates changes in the structure of foreign trade relations. A methodology for the study of foreign economic relations and the development of a trade region based on them using network graphs and a cluster approach is proposed, which makes it possible to identify stable groups of goods and, on their basis, assess the main trends in changes and the potential for the development of foreign economic activity in the region. We consider the features of calculating the adjacency matrix for constructing a graph, as well as identifying groups of vertices connected to each other in order to identify complete subgraphs — clicks, which allows us to identify the main time-stable commodity groups on which the foreign economic activity of the region depends. The described methodology should be used to improve the efficiency of building and describing the trade profile of the region in order to manage the development of foreign economic activity in the region, study the properties of product groups and their characteristics.
About the Author
A. N. KislyakovRussian Federation
Aleksey n. Kislyakov, Associate Professor of the Chair of Information Technology of Vladimir Branch of RANEPA, PhD in Technical Science
Vladimir
References
1. Goltseva A. Yu. Research of the market graph of the Russian stock market in the context of structural dynamics // New information technologies in automated systems [Novye informatsionnye tekhnologii v avtomatizirovannykh sistemakh]. 2014. N 17. P. 307–313 (in Rus).
2. Kislyakov A. N. Asymmetry of information in the tasks of analyzing socio-economic processes // Bulletin of the NSUEM [Vestnik NGUEU]. 2020. N 1. P. 64–75 (in Rus).
3. Kislyakov A. N. Graph clustering of behavioral activity of product users taking into account information asymmetry // News of Southwestern State University [Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta]. Series: Economics. Sociology. Management. 2020. N 3. P. 152– 163 (in Rus).
4. Mastitsky S. E. Analysis of time series using R. 2020 [Electronic resource]. URL: https://ranalytics.github.io/tsa-with-r (case date: 29.04.2021) (in Rus).
5. Moiseev A. K., Bondarenko P. A. Application of the economic complexity index in macro-fi cial models // Forecasting problems [Problemy prognozirovaniya]. 2020. N 3. P. 101–112 (in Rus).
6. Shitikov V.K., Mastitsky S. E. Classification, regression,Data Mining algorithms using R. 2017. [Electronic Resource]. URL: https://github.com/ranalytics/data-mining (case date: 29.04.2021) (in Rus).
7. Franklin J. The elements of statistical learning: data mining, inference and prediction // The Mathematical Intelligencer. 2003. Vol. 27. P. 83–85.
8. Hausmann R., Hidalgo C., Bustos S., Coscia M. at al. The Atlas of Economic Complexity: Mapping Paths to Prosperity. Cambridge : Center for International Development, Harvard University, MIT, 2011. P. 108–358.
9. Kislyakov A., Tikhonuyk N. Principles for Development of Predictive Stability Models of Social and Economic Systems on the basis of DTW // First Conference on Sustainable Development: Industrial Future of Territories (IFT 2020), 2020. Vol. 208, N 08001.
10. Kislyakov A.N., Filimonova N.M., Omarova N. Yu. Development of Predictive Models of SocioEconomic Systems Based on Decision Trees with Multivariate Response // Advances in Economics, Business and Management Research (AEBMR). Proceedings of International Scientific and Practical Conference “Russia 2020 — a new reality: economy and society”. 2021. P. 198–203.
11. Galit Shm. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. New Jersey : Wiley, 2017.
12. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2-nd ed. Springer, 2017.
Review
For citations:
Kislyakov A.N. Graph Methods for Describing the Trade Profile of a Region. Administrative Consulting. 2022;(2):70-80. (In Russ.) https://doi.org/10.22394/1726-1139-2022-2-70-80