국립부경대학교 미래 인터넷 및 빅데이터 응용 연구실

Title: Systemic Analysis of the QS International Research Network Indicator Using Big Data: Regional Inequalities and Recommendations for Improved University Rankings

https://ieeexplore.ieee.org/document/11050413

Citation

T. Kim and T. -K. Kim, "Systemic Analysis of the QS International Research Network Indicator Using Big Data: Regional Inequalities and Recommendations for Improved University Rankings," in IEEE Access, vol. 13, pp.111335-111353, 2025, doi: 10.1109/ACCESS.2025.3583236

Abstract:

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.

IEEE Keywords

Correlation, Measurement, Indexes, Collaboration, Europe, Ranking (statistics), Correlation coefficient, Big Data, Social sciences, Asia

Index Terms

Research Network, International Research, International Research Network, Regression Analysis, Correlation Analysis, Social Sciences, Predictive Power, Scatter Plot, Systematic Bias, Multiple Partners, Global Research, South Korean, Social Management, Methodological Inconsistencies, Korean University, Distribution Of Scores, Collaborative Research, International Network, International Collaboration, Global Engagement, Academic Reputation, Global Ranking, International Research Collaboration, Internationalization, English-speaking Countries, Decline In Scores, Ranking System, Meaningful Collaboration

Author Keywords

Big data analytics, higher education, international research network, international research network (IRN), QS world university rankings, regional inequality, research evaluation, university rankings

빅데이터 분석, 고등교육, 국제연구네트워크, 국제연구네트워크(IRN), QS 세계대학순위, 지역불평등, 연구평가, 대학순위, QS랭킹, 대학랭킹