Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Vícevrstvá centralita blízkosti× | Centralita blízkosti× | |
|---|---|---|
| Obor | Analýza sítí | Analýza sítí |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2013–2014 | 1950 (formalized 1979) |
| Tvůrce≠ | Kivela, M. et al.; De Domenico, M. et al. | Bavelas, A.; formalized by Freeman, L. C. |
| Typ≠ | Centrality measure for multilayer networks | Node-level centrality index |
| Původní zdroj≠ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Další názvy | multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Příbuzné≠ | 5 | 6 |
| Shrnutí≠ | Multilayer closeness centrality extends the classical closeness centrality measure to networks that contain multiple types of relationships or interaction contexts (layers). Rather than treating each layer in isolation, it computes how quickly a node can reach all others by traversing any combination of available layers, revealing nodes that are structurally efficient connectors across the full network 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. |
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