Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Dinamiskā kopienu noteikšana× | Daudzslāņu kopienu noteikšana× | |
|---|---|---|
| Nozare | Tīklu analīze | Tīklu analīze |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2010 (key formalization); earlier work 2002–2009 | 2010–2014 |
| Autors≠ | Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002) | Mucha, P. J. et al.; Kivela, M. et al. |
| Tips≠ | Graph clustering / community discovery | Community detection algorithm for multilayer networks |
| Pirmavots≠ | 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 ↗ | 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 | DCD, temporal community detection, evolving community detection, dynamic graph clustering | multilayer clustering, multiplex community detection, cross-layer community detection, MCD |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research. | 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. |
| ScholarGateDatu kopa ↗ |
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