From Data Footprints to Researcher Fingerprints: Temporal Ego-centric Research Group Detection in Scientific Knowledge Graphs

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.