Sammenlign metoder

	Multilayer PageRank	Multiplex Netværksanalyse
Fagområde	Netværksanalyse	Netværksanalyse
Familie	Machine learning	Machine learning
Oprindelsesår	2015	2014
Ophavsperson	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.	Kivela, M.; Boccaletti, S. et al.
Type	Centrality measure (random-walk-based)	Structural network model
Oprindelig kilde	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.	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.
Aliasser	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Relaterede	5	6
Resumé	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.