Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzslāņu starpposmu centralitāte× | Daudzslāņu kopienu noteikšana× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2013–2014 | 2010–2014 |
| Autors≠ | De Domenico, M.; Kivelä, M.; Arenas, A. et al. | Mucha, P. J. et al.; Kivela, M. et al. |
| Tips≠ | Centrality measure (multilayer extension) | Community detection algorithm for multilayer networks |
| Pirmavots≠ | De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Gómez, S., & Arenas, A. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022. 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 ↗ |
| Citi nosaukumi | MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality | multilayer clustering, multiplex community detection, cross-layer community detection, MCD |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Multilayer betweenness centrality extends the classical betweenness measure to networks with multiple types of relationships — or layers — by computing how often a node lies on shortest paths that can traverse any layer or switch between layers. It identifies brokers and bridges whose influence spans distinct interaction domains simultaneously. | Multilayer community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss. |
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