Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika temporālās multiplikācijas tīklu analīze× | Temporālā kopienu noteikšana× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2012–2014 | 2010 |
| Autors≠ | Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors) | Mucha, P. J. et al. |
| Tips≠ | Structural and dynamic network analysis | Network clustering algorithm |
| 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 ↗ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ |
| Citi nosaukumi | TMNA, time-varying multiplex network analysis, dynamic multiplex network analysis, temporal multilayer network analysis | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Temporal multiplex network analysis studies relational systems in which actors are connected by multiple distinct types of relationships that all evolve over time. By simultaneously tracking layer heterogeneity and temporal dynamics, the method reveals how different interaction channels co-evolve, which actors hold persistent cross-layer influence, and how structural changes propagate across relationship types and time periods. | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. |
| ScholarGateDatu kopa ↗ |
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