Salīdzināt metodes

	Daudzslāņu tuvuma centrālisms	Daudzslāņu tīklu analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2013–2014	2014
Autors	Kivela, M. et al.; De Domenico, M. et al.	Kivela, M.; Boccaletti, S. et al.
Tips	Centrality measure for multilayer networks	Structural network model
Pirmavots	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.	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.
Citi nosaukumi	multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Saistītās	5	6
Kopsavilkums	Multilayer closeness centrality extends the classical closeness centrality measure to networks that contain multiple types of relationships or interaction contexts (layers). Rather than treating each layer in isolation, it computes how quickly a node can reach all others by traversing any combination of available layers, revealing nodes that are structurally efficient connectors across the full network system.	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.