Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzslāņu starpposmu centralitāte× | Daudzslāņu tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2013–2014 | 2014 |
| Autors≠ | De Domenico, M.; Kivelä, M.; Arenas, A. et al. | Kivela, M.; Boccaletti, S. et al. |
| Tips≠ | Centrality measure (multilayer extension) | Structural network model |
| 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 | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA |
| Saistītās≠ | 5 | 6 |
| 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. | 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. |
| ScholarGateDatu kopa ↗ |
|
|