Mapping leadership and communities in EU-funded research through network analysis

TL;DR

The paper addresses how EU-funded research networks evolve, focusing on leadership roles and stable communities in hydrogen-sector collaborations. It develops a network-analysis pipeline that converts two-mode project–organization data into weighted one-mode networks via $A = W^T W$, computes centrality and coreness, and applies temporal consensus community-detection to track evolution. Applied to Horizon 2020/Europe data with the North Adriatic Hydrogen Valley case, it identifies key leaders, evolving communities, and patterns distinguishing market- vs technology-oriented projects. The findings offer policymakers and industry partners a transferable framework to foster sustained collaboration and to inform Horizon Dashboard visualizations.

Abstract

Horizon 2020 and Horizon Europe the EU programs supporting research and innovation through collaboration between companies, academic institutions, and research organisations. This paper introduces a novel methodology using open data on Horizon programs to analyse collaborations, leadership roles, and their evolution, with a focus on the North Adriatic Hydrogen Valley project in the hydrogen energy sector. The methodology employs network analysis, transforming tabular data into weighted networks that represent collaborations between organisations. Centrality measures and community detection algorithms identify influential organisations and stable partnerships over time. To ensure robust and reliable results, the methodology addresses challenges such as input-ordering bias and result variability, while the exploration of the solution space enhances the accuracy of identified collaboration patterns. The case study reveals key leaders and stable communities within the hydrogen energy sector, providing valuable insights for policymakers and organisations fostering innovation through sustained collaborations. The proposed methodology effectively identifies influential organisations and tracks the stability of research collaborations. The insights gained are valuable for policymakers and organisations seeking to foster innovation through sustained partnerships. This approach can be extended to other sectors, offering a framework for understanding the impact of EU research funding on collaboration and leadership dynamics.

Figures (11)

• Figure 1: A schematic representation of the North Adriatic Hydrogen Valley project. Testbeds are primarily driven by industrial partners, while collaboration with policymakers, universities, and research organisations is crucial for enabling innovation.
• Figure 2: Schematic view of a project as two-mode network (left) and as one-mode network (right). Uppercase letters A, B, C, D, E represent organisations. The green square denoted with P represents a project. Edge width is proportional to the weight, i.e. the value each organisation brings to the project. The sum of weights in the two-mode network is equal to the sum of weights in the one-mode network.
• Figure 3: Confidence intervals associated with different solutions in the solution space.
• Figure 4: Size of the networks $G_{y}$ represented by the number of organisations (horizontal axis) and the number of collaborations (vertical axis) and total investment per year (bubble size). Between 2016 and 2024 there is an increase in the number of organisations from 83 to 648 and a proportional rise in collaborations as represented by weights, rising from 547 to 6628. Current data for projects continuing in years 2025 to 2029 suggest further growth in the total value of projects.
• Figure 5: Comparison between technology-oriented and market-oriented project. The value is measured by netEcContribution; groups are identified by the AI generated labels: $G^{M}_{y}$ and $G^{T}_{y}$