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The Analysis of Artificial Neural Network Structure Recovery Possibilities Based on the Theory of Graphs

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Abstract

In the paper, the problem of the possibility of recovering the unknown structure of artificial neural networks (ANNs) using the theory of graphs is investigated. The key ANN concepts, their typical architectures, and differences are considered. The application of the theory of the graph tool for solving the problem of detecting an ANN structure is substantiated, and examples of comparing different ANN architectures and graph types are presented. It is proposed to use the methods of the spectral graph theory and the graph signal processing as tools for analyzing the ANN structure.

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Funding

The study was carried out within the State Assignment for Basic Research, project no. 0784-2020-0026, and was supported by the Collateral Agreement to the Agreement on the Grant from the Federal Budget for Financial Support of the State Assignment for the Provision of Public Services (execution of works) no. 075-03-2020-158/2 dated March 17, 2020 (internal no. 075-GZ/Shch4575/784/2).

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  1. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    D. S. Lavrova & A. A. Shtyrkina

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  1. D. S. Lavrova
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  2. A. A. Shtyrkina
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Correspondence to D. S. Lavrova or A. A. Shtyrkina.

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The authors declare that they have no conflicts of interest.

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Translated by N. Semenova

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Lavrova, D.S., Shtyrkina, A.A. The Analysis of Artificial Neural Network Structure Recovery Possibilities Based on the Theory of Graphs. Aut. Control Comp. Sci. 54, 977–982 (2020). https://doi.org/10.3103/S0146411620080222

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