Version 1
: Received: 10 July 2023 / Approved: 11 July 2023 / Online: 12 July 2023 (07:51:06 CEST)
How to cite:
Avros, R.; Keshet, S.; Kitai, D. T.; Vexler, E.; Volkovich, Z. Detecting Pseudo Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph. Preprints2023, 2023070777. https://doi.org/10.20944/preprints202307.0777.v1
Avros, R.; Keshet, S.; Kitai, D. T.; Vexler, E.; Volkovich, Z. Detecting Pseudo Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph. Preprints 2023, 2023070777. https://doi.org/10.20944/preprints202307.0777.v1
Avros, R.; Keshet, S.; Kitai, D. T.; Vexler, E.; Volkovich, Z. Detecting Pseudo Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph. Preprints2023, 2023070777. https://doi.org/10.20944/preprints202307.0777.v1
APA Style
Avros, R., Keshet, S., Kitai, D. T., Vexler, E., & Volkovich, Z. (2023). Detecting Pseudo Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph. Preprints. https://doi.org/10.20944/preprints202307.0777.v1
Chicago/Turabian Style
Avros, R., Evgeny Vexler and Zeev Volkovich. 2023 "Detecting Pseudo Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph" Preprints. https://doi.org/10.20944/preprints202307.0777.v1
Abstract
Ensuring the integrity of scientific literature is essential for advancing knowledge and research. However, the credibility and trustworthiness of scholarly publications are compromised by manipulated citations. Traditional methods, such as manual inspection and basic statistical analyses, have limitations in detecting intricate patterns and subtle manipulations of citations. In recent years, network-based approaches have emerged as promising techniques for identifying and understanding citation manipulation. This study introduces a novel method to identify potential citation manipulation in academic papers using perturbations of a deep embedding model. The key idea is to reconstruct meaningful connections represented by citations within a network by exploring slightly longer alternative paths. These indirect pathways enable the recovery of original and reliable citations while estimating their trustworthiness. The investigation takes a comprehensive approach to link prediction, leveraging the consistent behavior of prominent connections when exposed to network perturbations. Through numerical experiments, the method demonstrates a high capability to identify reliable citations as the core of the analyzed data and to raise suspicions about unreliable references that may have been manipulated.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
