Beyond Content: How Author Network Centrality Drives Citation Disparities in Top AI Conferences

TL;DR

This paper investigates how authors' positions in collaboration networks influence citation disparities in top AI conferences, introducing a new centrality metric and demonstrating its predictive power for citation impact.

Contribution

It introduces a novel centrality measure, HCTCD, and employs beta regression to model citation percentiles, revealing the significant role of network position in citation disparities.

Findings

Long-term author centrality strongly predicts citation impact.

Team-level centrality aggregation explains citation variance more effectively.

Incorporating centrality features improves citation prediction models.

Abstract

While scholarly citations are pivotal for assessing academic impact, they often reflect systemic biases beyond research quality. This study examines a critical yet underexplored driver of citation disparities: authors' structural positions within scientific collaboration networks. Through a large-scale analysis of 17,942 papers from three top-tier machine learning conferences (NeurIPS, ICML, ICLR) published between 2005 and 2024, we quantify the influence of author centrality on citations. Methodologically, we advance the field by employing beta regression to model citation percentiles, which appropriately accounts for the bounded nature of citation data. We also propose a novel centrality metric, Harmonic Closeness with Temporal and Collaboration Count Decay (HCTCD), which incorporates temporal decay and collaboration intensity. Our results robustly demonstrate that 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. Importantly, team-level centrality aggregation, particularly through exponentially weighted summation, explains citation variance more effectively than conventional rank-based approaches, underscoring the primacy of collective network connectivity over individual prominence. Integrating centrality features into machine learning models yields a 2.4% to 4.8% reduction in prediction error (MSE), confirming their value beyond content-based benchmarks. These findings challenge entrenched evaluation paradigms and advocate for network-aware assessment frameworks to mitigate structural inequities in scientific recognition.