Bandingkan metode

	Sentralitas Antara Lapisan Ganda	Sentralitas Kedekatan Multilapis
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2013–2014	2013–2014
Pencetus	De Domenico, M.; Kivelä, M.; Arenas, A. et al.	Kivela, M. et al.; De Domenico, M. et al.
Tipe	Centrality measure (multilayer extension)	Centrality measure for multilayer networks
Sumber perintis	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
Terkait	5	5
Ringkasan	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.