Assessing citation appropriateness through masked graph autoencoder

This study proposes a novel network-based detection method for assessing citation reliability and identifying manipulative practices. Applying a Masked Graph Autoencoder, the approach learns expected citation patterns by iteratively masking and reconstructing network edges. High reconstruction error...

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Bibliographic Details
Published in:Procedia computer science Vol. 270; pp. 273 - 281
Main Authors: Avros, Renata, Volkovich, Zeev
Format: Journal Article
Language:English
Published: Elsevier B.V 2025
Subjects:
Citation reliability
masked graph autoencoder
network perturbations
Citation reliability
network perturbations
masked graph autoencoder
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary:This study proposes a novel network-based detection method for assessing citation reliability and identifying manipulative practices. Applying a Masked Graph Autoencoder, the approach learns expected citation patterns by iteratively masking and reconstructing network edges. High reconstruction errors flag manipulated citations, while genuine citations are accurately reconstructed due to their structural robustness. The reconstruction error provides a quantitative measure of reliability, enabling automated, large-scale detection of citation anomalies. This graph-based deep learning approach captures complex network relationships, reducing the need for manual audits.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2025.09.146