Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph
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 limi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/18/3820 |
| _version_ | 1797579029051604992 |
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| author | Renata Avros Saar Keshet Dvora Toledano Kitai Evgeny Vexler Zeev Volkovich |
| author_facet | Renata Avros Saar Keshet Dvora Toledano Kitai Evgeny Vexler Zeev Volkovich |
| author_sort | Renata Avros |
| collection | DOAJ |
| description | 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, to some extent, longer alternative paths. These indirect pathways enable the recovery of 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. This research presents a refined method for tackling the urgent problem of citation manipulation in academic papers. It harnesses statistical sampling and graph-embedding techniques to evaluate the credibility of scholarly publications with a substantial assessment of the whole citation graph. |
| first_indexed | 2024-03-10T22:30:03Z |
| format | Article |
| id | doaj.art-7551725aac8746ddab2b19d160326eb9 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T22:30:03Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-7551725aac8746ddab2b19d160326eb92023-11-19T11:48:04ZengMDPI AGMathematics2227-73902023-09-011118382010.3390/math11183820Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation GraphRenata Avros0Saar Keshet1Dvora Toledano Kitai2Evgeny Vexler3Zeev Volkovich4Software Engineering Department, Braude College of Engineering, Karmiel 21982, IsraelSoftware Engineering Department, Braude College of Engineering, Karmiel 21982, IsraelSoftware Engineering Department, Braude College of Engineering, Karmiel 21982, IsraelSoftware Engineering Department, Braude College of Engineering, Karmiel 21982, IsraelSoftware Engineering Department, Braude College of Engineering, Karmiel 21982, IsraelEnsuring 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, to some extent, longer alternative paths. These indirect pathways enable the recovery of 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. This research presents a refined method for tackling the urgent problem of citation manipulation in academic papers. It harnesses statistical sampling and graph-embedding techniques to evaluate the credibility of scholarly publications with a substantial assessment of the whole citation graph.https://www.mdpi.com/2227-7390/11/18/3820graph embeddingmanipulated citationsnetwork perturbation |
| spellingShingle | Renata Avros Saar Keshet Dvora Toledano Kitai Evgeny Vexler Zeev Volkovich Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph Mathematics graph embedding manipulated citations network perturbation |
| title | Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph |
| title_full | Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph |
| title_fullStr | Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph |
| title_full_unstemmed | Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph |
| title_short | Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph |
| title_sort | detecting pseudo manipulated citations in scientific literature through perturbations of the citation graph |
| topic | graph embedding manipulated citations network perturbation |
| url | https://www.mdpi.com/2227-7390/11/18/3820 |
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