European Union Machine Learning Research: A Network Analysis of Collaboration in Higher Education (2020–2024)

Lilia-Eliana Popescu-Apreutesei, Mihai-Sorin Iosupescu, Doina Fotache, Sabina-Cristiana Necula

The intense rising of machine learning in the previous years, bolstered by post-COVID-19 digitalization, left some of us pondering upon the transparency practices involving projects sourced from European Union funds. This study focuses on the European Union research clusters and trends in the ecosystem of higher education institutions (HEIs). The manually curated dataset of bibliometric data from 2020 to 2024 was analyzed in steps, from the traditional bibliometric indicators to natural language processing and collaboration networks. Centrality metrics, including degree, betweenness, closeness, and eigenvector centrality, and a three-way-intersection of community detection algorithms were computed to quantify the influence and the connectivity of institutions in different communities in the collaborative research networks. In the EU context, results indicate that institutions such as Universidad Politecnica de Madrid, the University of Cordoba, and Maastricht University frequently occupy central positions, echoing their role as local or regional hubs. At the global level, prominent North American and UK-based universities (e.g., University of Pennsylvania, Columbia University, Imperial College London) also remain influential, standing as a witness to their enduring influence in transcontinental research. Clustering outputs further confirmed that biomedical and engineering-oriented lines of inquiry often dominated these networks. While multiple mid-ranked institutions do appear at the periphery, the data highly implies that large-scale initiatives gravitate toward well-established players. Although the recognized centers provide specialized expertise and resources, smaller universities typically rely on a limited number of niche alliances.