Papers › Mapping scientific communities at scale
Mapping scientific communities at scale
Victor Barbier, Eric Jeangirard
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This study introduces a novel methodology for mapping scientific communities at scale, addressing challenges associated with network analysis in large bibliometric datasets. By leveraging enriched publication metadata from the French research portal scanR and applying advanced filtering techniques to prioritize the strongest interactions between entities, we construct detailed, scalable network maps. These maps are enhanced through systematic disambiguation of authors, affiliations, and topics using persistent identifiers and specialized algorithms. The proposed framework integrates Elasticsearch for efficient data aggregation, Graphology for network spatialization (Force Atltas2) and community detection (Louvain algorithm) and VOSviewer for network vizualization. A Large Language Model (Mistral Nemo) is used to label the communities detected and OpenAlex data helps to enrich the results with citation counts estimation to detect hot topics. This scalable approach enables insightful exploration of research collaborations and thematic structures, with potential applications for strategic decision-making in science policy and funding. These web tools are effective at the global (national) scale but are also available (and can be integrated via iframes) on the perimeter of any French research institution (from large research organisms to any laboratory). The scanR community analysis tool is available online [https://scanr.enseignementsup-recherche.gouv.fr/networks/get-started](https://scanr.enseignementsup-recherche.gouv.fr/networks/get-started). All tools and methodologies are open-source on the repo [https://github.com/dataesr/scanr-ui](https://github.com/dataesr/scanr-ui)
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