Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Centralita blízkosti× | Mezilehlostní centralita× | |
|---|---|---|
| Obor | Analýza sítí | Analýza sítí |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1950 (formalized 1979) | 1977 |
| Tvůrce≠ | Bavelas, A.; formalized by Freeman, L. C. | Freeman, L. C. |
| Typ≠ | Node-level centrality index | Centrality measure |
| Původní zdroj≠ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Další názvy | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Příbuzné | 6 | 6 |
| Shrnutí≠ | 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. | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. |
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