Analysis of the sustainability of economic networks through graph models and machine learning methods in the context of digital transformation

Abstract

This article examines the analysis of the sustainability of economic networks using graph models and machine learning methods in the context of digital transformation. The relevance of the research is due to the increasing complexity of economic systems, the need for early detection of vulnerabilities and the optimal strategy for network development in a dynamically changing environment. The Introduction section substantiates the importance of using new analysis methods for economic networks, which allows us to take into account not only static, but also dynamic aspects of the interrelationships between the elements of the system. This work is aimed at developing an integrated approach combining theoretical research and practical analysis tools. The Methods section outlines the principles of constructing graph models that structure economic data, as well as machine learning algorithms used to predict changes in the network and assess system stability. Methods of cluster analysis, regression, and neural networks are used to identify key factors affecting the stability of interactions within an economic network. The main focus is on adapting modern machine learning algorithms to the specifics of economic processes. The "Results" section demonstrates the effectiveness of the proposed approach based on a comprehensive analysis of real data, which revealed patterns that contribute to the formation of sustainable economic structures. The results obtained confirm the possibility of predicting critical changes in networks and allowed us to develop recommendations to increase their stability. Finally, the Discussion section analyzes the data obtained from the standpoint of the theoretical significance and practical applicability of the study. It is shown that the integration of graph models and machine learning methods contributes to a deeper understanding of the mechanisms of functioning of economic networks in the context of digital transformation, which opens up new prospects for development in this area and provides a basis for further empirical research. The results obtained significantly expand the possibilities of analyzing economic systems, allowing them to quickly respond to changes within the network. The use of integrated methods helps optimize management decisions and increase the sustainability of the economy at the micro and macro levels. Assessing the impact of digital transformation on the dynamics of interactions in economic networks is becoming an important factor in developing strategic development measures. Further research will make it possible to refine the modeling of processes and integrate new data, providing a more accurate prediction of economic stability, which will significantly increase the effectiveness of the analysis methods.