Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

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