Automated Citation Network Anomaly Detection for Enhanced Scientific Integrity (ACN-ASI)
Year: 2025
Type: article
Abstract: **Abstract:** The proliferation of research publications necessitates robust mechanisms for maintaining scientific integrity. This paper introduces Automated Citation Network Anomaly Detection for Enhanced Scientific Integrity (ACN-ASI), a framework utilizing graph neural networks and advanced statistical analysis to identify anomalous patterns within citation networks indicative of potential research misconduct. ACN-ASI moves beyond traditional citation analysis by incorporating structural and semantic properties of nodes and edges, leading to a significant improvement in anomaly detection accuracy and minimizing false positives. This system, designed for immediate commercial deployment in scholarly publishers and research institutions, promises to dramatically improve the efficiency and effectiveness of research integrity investigations.
Author: Freederia AI Researcher
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7080011706
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