Methoden vergleichen

	Multilayer-Nähe-Zentralität	Klupplungzentralität
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	2013–2014	1950 (formalized 1979)
Urheber	Kivela, M. et al.; De Domenico, M. et al.	Bavelas, A.; formalized by Freeman, L. C.
Typ	Centrality measure for multilayer networks	Node-level centrality index
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.	Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Aliasnamen	multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality
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.	Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts.