Author Network Centrality Drives Citation Disparities in AI Conferences
Research Paper#AI, Machine Learning, Citation Analysis, Network Science🔬 Research|Analyzed: Jan 4, 2026 00:01•
Published: Dec 26, 2025 02:24
•1 min read
•ArXivAnalysis
This paper investigates how the position of authors within collaboration networks influences citation counts in top AI conferences. It moves beyond content-based evaluation by analyzing author centrality metrics and their impact on citation disparities. The study's methodological advancements, including the use of beta regression and a novel centrality metric (HCTCD), are significant. The findings highlight the importance of long-term centrality and team-level network connectivity in predicting citation success, challenging traditional evaluation methods and advocating for network-aware assessment frameworks.
Key Takeaways
- •Author network centrality significantly impacts citation counts in top AI conferences.
- •Long-term centrality metrics are more predictive of citation success than short-term ones.
- •Team-level network connectivity is crucial for explaining citation variance.
- •The study proposes a novel centrality metric (HCTCD) and uses beta regression for citation analysis.
- •Integrating centrality features improves citation prediction accuracy, suggesting the need for network-aware evaluation frameworks.
Reference / Citation
View Original"Long-term centrality exerts a significantly stronger effect on citation percentiles than short-term metrics, with closeness centrality and HCTCD emerging as the most potent predictors."