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Fake Post Detection Using Graph Neural Networks

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Abstract—

The article is devoted to the study of graph neural networks as a separate field and the possibility of their application to solve such an urgent cybersecurity problem as the detection of fake posts. The authors’ implementation of a graph neural network for detecting fake posts is described. The results of experimental studies showing the effectiveness of graph neural networks for solving the problem are presented.

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Funding

This work was supported by the Scholarship of the President of the Russian Federation to Young Scientists and Graduate Students, project no. SP-1932.2019.5.

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Authors and Affiliations

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

    O. A. Izotova & D. S. Lavrova

Authors
  1. O. A. Izotova
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  2. D. S. Lavrova
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Corresponding authors

Correspondence to O. A. Izotova or D. S. Lavrova.

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The authors declare that they have no conflicts of interest.

Additional information

Translated by O. Pismenov

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Izotova, O.A., Lavrova, D.S. Fake Post Detection Using Graph Neural Networks. Aut. Control Comp. Sci. 55, 1215–1221 (2021). https://doi.org/10.3103/S0146411621080393

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  • DOI: https://doi.org/10.3103/S0146411621080393

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