Methoden vergleichen

	Multilayer-Nähe-Zentralität	Multiplex-Netzwerkanalyse
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	2013–2014	2014
Urheber	Kivela, M. et al.; De Domenico, M. et al.	Kivela, M.; Boccaletti, S. et al.
Typ	Centrality measure for multilayer networks	Structural network model
Wegweisende Quelle	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.
Aliasnamen	multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Verwandt	5	6
Zusammenfassung	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.