Salīdzināt metodes

	Daudzslāņu PageRank	Daudzslāņu starpposmu centralitāte
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2015	2013–2014
Autors	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.	De Domenico, M.; Kivelä, M.; Arenas, A. et al.
Tips	Centrality measure (random-walk-based)	Centrality measure (multilayer extension)
Pirmavots	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.	De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Gómez, S., & Arenas, A. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022.
Citi nosaukumi	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank	MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality
Saistītās	5	5
Kopsavilkums	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.	Multilayer betweenness centrality extends the classical betweenness measure to networks with multiple types of relationships — or layers — by computing how often a node lies on shortest paths that can traverse any layer or switch between layers. It identifies brokers and bridges whose influence spans distinct interaction domains simultaneously.