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| Pusat Darjah Berwajaran× | Analisis Rangkaian Sosial× | |
|---|---|---|
| Bidang | Analisis Rangkaian | Analisis Rangkaian |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2004 | 1934 (sociometry); 1994 (modern formalization) |
| Pengasas≠ | Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A. | Moreno, J.L.; formalized by Wasserman & Faust |
| Jenis≠ | Centrality measure for weighted networks | Structural/relational analysis framework |
| Sumber perintis≠ | Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| Alias | node strength, strength centrality, weighted node degree, WDC | SNA, network analysis, sociometric analysis, relational analysis |
| Berkaitan≠ | 6 | 5 |
| Ringkasan≠ | Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score. | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. |
| ScholarGateSet data ↗ |
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