Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Diverģētās multiplikācijas tīklu analīze× | Daudzslāņu tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2013–2014 | 2014 |
| Autors≠ | Kivela, M.; De Domenico, M. et al. | Kivela, M.; Boccaletti, S. et al. |
| Tips≠ | Multi-layer directed graph framework | Structural network model |
| Pirmavots | 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 ↗ |
| Citi nosaukumi | directed multilayer network analysis, directed multiplex graphs, asymmetric multiplex network analysis, DMNA | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Directed multiplex network analysis models systems where the same set of nodes are connected by multiple types of directed (asymmetric) relationships across distinct layers — such as citation flows, information cascades, or authority hierarchies co-existing simultaneously. It extends multiplex network analysis by preserving both layer identity and edge directionality, enabling richer structural and dynamic insights. | 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 ↗ |
|
|