Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Redes de Coautoría× | Análisis de cocitación× | |
|---|---|---|
| Campo | Bibliometría | Bibliometría |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2001 | 1973 |
| Autor original≠ | Mark E. J. Newman and others | Henry Small |
| Tipo | Method | Method |
| Fuente seminal≠ | Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409. DOI ↗ | Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269. DOI ↗ |
| Alias≠ | collaboration network, authorship network, research collaboration mapping | co-citation mapping, historiograph, direct citation, citation pair analysis |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | Co-authorship network analysis is a method that maps research collaboration patterns by treating authors as nodes and co-authored papers as edges in a network graph. The structure, density, and centrality patterns of this network reveal how researchers connect, collaborate across institutions and disciplines, and form research communities. Pioneered formally by Newman (2001), co-authorship analysis provides quantitative insights into the social fabric of science, revealing collaboration patterns, identifying scientific leaders, and detecting institutional or disciplinary boundaries. | Co-citation analysis is a method that identifies the intellectual structure of a research domain by examining how frequently pairs of documents are cited together in other publications. When two papers are frequently cited together in the literature, they are considered co-cited, indicating they are conceptually related or influential within the same research community. Developed by Henry Small in 1973, co-citation analysis maps the 'invisible colleges' of science—networks of researchers working on related problems—and reveals how knowledge domains evolve over time. |
| ScholarGateConjunto de datos ↗ |
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