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Spotting Suspicious Academic Citations Using Self-Learning Graph Transformers
Version 1
: Received: 21 December 2023 / Approved: 22 December 2023 / Online: 22 December 2023 (14:33:53 CET)
A peer-reviewed article of this Preprint also exists.
Avros, R.; Haim, M.B.; Madar, A.; Ravve, E.; Volkovich, Z. Spotting Suspicious Academic Citations Using Self-Learning Graph Transformers. Mathematics 2024, 12, 814. Avros, R.; Haim, M.B.; Madar, A.; Ravve, E.; Volkovich, Z. Spotting Suspicious Academic Citations Using Self-Learning Graph Transformers. Mathematics 2024, 12, 814.
Abstract
The study introduces a novel method to identify potential citation manipulation in academic papers using perturbations of a deep embedding model, incorporating Graph Masked Autoencoders. This approach integrates textual information with graph connectivity evidence, resulting in a more sophisticated model of citation distribution. By training a deep network using partial data and reconstructing masked connections, the method leverages the inherent characteristics of central connections under network perturbations. Quantitative evaluations demonstrate its remarkable ability to pinpoint trustworthy citations in the analyzed data and raise concerns about potentially unreliable references due to potential manipulation.
Keywords
graph masked autoencoders; manipulated citations, network perturbation
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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