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| Analisis Rangkaian Kolaborasi Penulis× | Analisis Kebersamaan Kata Kunci× | |
|---|---|---|
| Bidang | Bibliometrik | Bibliometrik |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 2001 | 2000s |
| Pengasas≠ | Mark E. J. Newman and others | Bibliometric research community |
| Jenis | Method | Method |
| Sumber perintis≠ | Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409. DOI ↗ | Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166. DOI ↗ |
| Alias≠ | collaboration network, authorship network, research collaboration mapping | term co-occurrence, keyword network analysis, thematic analysis, term clustering |
| Berkaitan | 4 | 4 |
| Ringkasan≠ | 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. | Keyword co-occurrence analysis is a text mining and bibliometric method that identifies research themes and their relationships by analyzing how frequently terms or keywords appear together in abstracts, titles, or indexed keywords of scientific publications. When two keywords appear together frequently, they are considered co-occurring, indicating a shared thematic or conceptual relationship. This method rapidly reveals the topical structure of a research field without relying on formal classifications, making it particularly useful for detecting emerging research areas and understanding disciplinary boundaries. |
| ScholarGateSet data ↗ |
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