Graph Condensation for Large Factor Models

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The paper proposes an original method for processing large factor models based on graph condensation using machine learning models and artificial neural networks. The proposed mathematical apparatus can be used in problems of planning and managing complex organizational and technical systems, in optimizing large socio-economic objects on the scale of state sectors, to solve problems of preserving the health of the nation (searching for compatibility when taking medications, optimizing resource provision for healthcare).

About the authors

B. N. Chetverushkin

Keldysh Institute of Applied Mathematics (Russian Academy of Sciences)

Author for correspondence.
Email: office@keldysh.ru

Academician of the RAS

Russian Federation, Moscow

V. A. Sudakov

Keldysh Institute of Applied Mathematics (Russian Academy of Sciences)

Email: sudakov@ws-dss.com
Russian Federation, Moscow

Yu. P. Titov

Moscow Aviation Institute (National Research University)

Email: kalengul@mail.ru
Russian Federation, Moscow

References

  1. Четверушкин Б.Н., Судаков В.А. Факторная модель для исследования сложных процессов // Доклады Академии наук. 2019. Т. 489. № 1. С. 17–21.
  2. Forrester J.W. Policies, decisions and information sources for modeling // European Journal of Operational Research. 1992. V. 59. № 1. P. 42–63.
  3. Honti G., Dörgő G., Abonyi J. Network analysis dataset of system dynamics models // Data in Brief. 2019. V. 27. P. 104723.
  4. Jain A., Murty M., Flynn P. Data Clustering: A Review // ACM Computing Surveys. 1999. V. 31. № 3.
  5. Lance G.N., Williams W.T. A general theory of classification sorting strategies in hierarchical system // Comp. J. 1967. № 9. P. 373–380.
  6. Kohonen T. Essentials of the self-organizing map // Neural Networks. 2013. V. 37. P. 52–65.
  7. Alam A., Ahamad M.K. K-Means Hybridization with Enhanced Firefly Algorithm for High-Dimension Automatic Clustering // Journal of Advanced Research in Applied Sciences and Engineering Technology. 2023. V. 33. № 3. P. 137–153.
  8. Reynolds D. Gaussian Mixture Models // Encyclopedia of Biometrics. Boston, MA: Springer, 2009.
  9. Нестеров В.А., Судаков В.А., Сыпало К.И., Титов Ю.П. Матрица нечетких корреспонденций модели авиационных перевозок // Известия Российской академии наук. Теория и системы управления. 2022. Т. 6. № 6. С. 95–102.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Russian Academy of Sciences