Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Svērta multipleksu tīklu analīze× | Daudzslāņu tīklu analīze× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads | 2014 | 2014 |
| Autors≠ | Battiston, F.; Kivela, M. et al. | Kivela, M.; Boccaletti, S. et al. |
| Tips≠ | Network analysis framework | Structural network model |
| Pirmavots≠ | Battiston, F., Nicosia, V., & Latora, V. (2014). Structural measures for multiplex networks. Physical Review E, 89(3), 032804. 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 | WMNA, weighted multilayer network analysis, weighted multi-relational network analysis, multiplex weighted graph analysis | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Weighted multiplex network analysis studies systems in which the same set of actors are connected through multiple types of relationships simultaneously, and each relationship carries a quantitative strength or frequency. By capturing both the variety and the intensity of ties across layers, it reveals patterns invisible to single-layer or unweighted network approaches. | 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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