Systemic Analysis of the QS International Research Network Indicator Using Big Data: Regional Inequalities and Recommendations for Improved University Rankings - Taewan Kim; Tae-Kook Kim (IEEE Access)
The International Research Network (IRN) indicator, introduced in the QS (Quacquarelli Symonds) World University Rankings 2024, has generated notable volatility and regional disparities in global university standings. This paper presents a systemic analysis of the IRN indicator across three ranking cycles (2023–2025) using big data methodologies, including descriptive statistics, scatter plots, university size analysis, a case study of South Korean universities in Social Sciences & Management, correlation, and regression analysis. The results reveal pronounced instability in IRN scores, with sharp year-to-year fluctuations and a marked concentration of top-ranked institutions in English-speaking and European regions-98 of the top 100 and 85% of the top 500 IRN-ranked universities originate from these areas. In addition to identifying structural and regional biases, this study examines how effectively IRN functions as a ranking metric, particularly in its ability to predict overall QS performance. Findings from regression analysis show that the contribution of IRN to the overall QS score is minimal, with its predictive power diminishing significantly in the 2025 ranking year. The South Korean case study highlights methodological inconsistencies, showing that the IRN formula disadvantages institutions with multiple partnerships in the same region. These observations are reinforced by correlation and regression analyses, which further confirm that IRN’s explanatory power for overall QS scores weakened in the 2025 ranking year. These findings underscore the need to refine the IRN indicator to enhance transparency, consistency, and inclusivity, thereby supporting a more equitable evaluation of global research networks.