Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis de Redes Sociales× | Centralidad de Cercanía× | |
|---|---|---|
| Campo | Análisis de redes | Análisis de redes |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1934 (sociometry); 1994 (modern formalization) | 1950 (formalized 1979) |
| Autor original≠ | Moreno, J.L.; formalized by Wasserman & Faust | Bavelas, A.; formalized by Freeman, L. C. |
| Tipo≠ | Structural/relational analysis framework | Node-level centrality index |
| Fuente seminal≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Alias | SNA, network analysis, sociometric analysis, relational analysis | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Relacionados≠ | 5 | 6 |
| Resumen≠ | 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. | Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts. |
| ScholarGateConjunto de datos ↗ |
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