20.01.2025
Mapping scientific communities at scale
« 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. (…) »
