Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Социальный сетевой анализ× | Центральность по близости× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1934 (sociometry); 1994 (modern formalization) | 1950 (formalized 1979) |
| Автор метода≠ | Moreno, J.L.; formalized by Wasserman & Faust | Bavelas, A.; formalized by Freeman, L. C. |
| Тип≠ | Structural/relational analysis framework | Node-level centrality index |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | SNA, network analysis, sociometric analysis, relational analysis | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Связанные≠ | 5 | 6 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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