Dias, C (2026) A pilot study into the application of Social Network Analysis on NPL’s publication data. NPL Report. IEA 36

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Abstract

This pilot study applies Social Network Analysis (SNA) to the National Physical Laboratory’s publication dataset (2000–2024) to better understand internal scientific collaboration, which is an area not captured by traditional citation‑based indicators. Using four network metrics (Degree, Degree per Paper, Betweenness Centrality, and Eigenvector Centrality), the study maps co‑authorship patterns across nine departments and five time periods, assessing how collaboration, influence, and structural inequality evolve over time.

Findings show a clear rise in collaborative activity, with increases in Degree and Degree per Paper indicating a more interconnected research workforce. Betweenness centrality grew early in the period, reflecting expanding interdepartmental bridging, while Eigenvector centrality fell sharply after 2004 before stabilising, signalling shifts in influential authorship. Inequality is present across all metrics, particularly in centrality measures, driven largely by lower‑connected authors. Departments such as AES, MMN, Time, TRM, and CBS display distinctive strengths, including high influence or strong brokerage roles.

The study highlights potential implications for organisational resilience, showing how SNA can identify key knowledge brokers, potential vulnerabilities, and links between collaboration structures and innovation outputs. It provides the context and background for deeper analysis in a forthcoming 2026 study.

Item Type:	Report/Guide (NPL Report)
NPL Report No.:	IEA 36
Divisions:	Strategy Directorate
Identification number/DOI:	10.47120/npl.IEA36
Last Modified:	22 Sep 2026 13:55
URI:	https://eprintspublications.npl.co.uk/id/eprint/10549