Salīdzināt metodes

	Daudzslāņu grādu centralitāte	Daudzslāņu PageRank
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2013–2014	2015
Autors	Kivelä, M.; De Domenico, M. et al.	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.
Tips	Centrality measure for multilayer networks	Centrality measure (random-walk-based)
Pirmavots	Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.	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.
Citi nosaukumi	multilayer degree, multiplex degree centrality, overlapping-layer degree centrality, MDC	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank
Saistītās	6	5
Kopsavilkums	Multilayer degree centrality extends the classic degree centrality measure to networks composed of multiple layers — such as networks representing different types of social ties, communication channels, or relationship contexts simultaneously. It quantifies how many connections a node has across one or all layers, revealing nodes that are influential not just in a single context but across the entire multi-relational structure.	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.