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| Többrétegű Közelségközpontúság× | Multiplex Hálózatok Elemzése× | |
|---|---|---|
| Tudományterület | Hálózatelemzés | Hálózatelemzés |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2013–2014 | 2014 |
| Megalkotó≠ | Kivela, M. et al.; De Domenico, M. et al. | Kivela, M.; Boccaletti, S. et al. |
| Típus≠ | Centrality measure for multilayer networks | Structural network model |
| Alapmű | 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 ↗ | 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 ↗ |
| Alternatív nevek | multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA |
| Kapcsolódó≠ | 5 | 6 |
| Összefoglaló≠ | 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. | Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities. |
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