Summary
Tracking how researchers work together and form groups over time helps build detailed profiles of their scientific careers. The authors developed a method that focuses on individual researchers and their immediate collaboration networks to detect research groups. This method uses data from scientific knowledge graphs and ensures the central researcher is always part of the groups found, making the analysis personal. They tested it with ten researchers from diverse fields and career stages, showing that it highlights meaningful collaboration structures. These groups can then be used to better understand roles and contributions in future research evaluations.
What this means in practice
- •For university research offices: Provide detailed collaboration structures centered on individual researchers to support fairer research performance reviews.
- •For scholarly database maintainers: Enhance profiles by integrating temporal egocentric research group detection to reveal evolving collaboration patterns.
Authors
Simone Angioni, Miriam Baglioni, Andrea Mannocci, Paolo Manghi
Abstract
Understanding who researchers collaborate with, when and how their research groups form, and how these evolve over time is a foundational prerequisite for constructing multidimensional, evidence-based researcher fingerprints -- a requirement that has become increasingly urgent in the context of ongoing reforms in research evaluation advocated by initiatives such as DORA, the Leiden Manifesto, and CoARA. In this paper, we propose an egocentric approach for fine-grained research group detection, operating at the scale of individual researchers' ego networks, as extracted from Scientific Knowledge Graphs (SKGs), and requiring the ego node to be present in all identified research groups -- a constraint that is not merely technical but reflects a commitment to researcher-centred analysis. Our approach builds on the Leiden algorithm as its computational core and is grounded in SKGs, which provide the heterogeneous, open scholarly metadata necessary to trace collaboration patterns at scale in a reproducible and transparent manner. We evaluate the proposed method through a user evaluation involving 10 researchers with diverse backgrounds and career stages, demonstrating its ability to surface meaningful and interpretable collaboration structures. The research groups identified constitute the structural scaffold upon which richer characterisations of scholarly identity -- including roles, seniority, contribution types, and publication patterns -- can be built in subsequent work, contributing to the broader goal of fairer and more transparent research assessment.