Comparer des méthodes

	Centralité d'intermédiarité multicouche	Centralité de Proximité Multicouche
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2013–2014	2013–2014
Auteur d'origine	De Domenico, M.; Kivelä, M.; Arenas, A. et al.	Kivela, M. et al.; De Domenico, M. et al.
Type	Centrality measure (multilayer extension)	Centrality measure for multilayer networks
Source fondatrice	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.	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.
Alias	MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality	multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality
Apparentées	5	5
Résumé	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.	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.