Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Vícevrstvý PageRank× | Analýza multiplexních sítí× | |
|---|---|---|
| Obor | Analýza sítí | Analýza sítí |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2015 | 2014 |
| Tvůrce≠ | De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al. | Kivela, M.; Boccaletti, S. et al. |
| Typ≠ | Centrality measure (random-walk-based) | Structural network model |
| Původní zdroj≠ | De Domenico, M., Sole-Ribalta, A., Omodei, E., Gomez, S., & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6, 6868. 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 ↗ |
| Další názvy | multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank | multiplex networks, multi-layer network analysis, multilayer network analysis, MNA |
| Příbuzné≠ | 5 | 6 |
| Shrnutí≠ | Multilayer PageRank extends the classic PageRank random-walk centrality to networks that contain multiple interconnected layers — such as a social network where people are connected simultaneously via friendship, professional ties, and online platforms. By allowing a virtual walker to jump both within and across layers, the algorithm identifies nodes that are influential across the entire multilayer structure, not just within any single layer. | 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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