Linganisha mbinu

	Uchanganuzi wa Kati wa Tabaka Nyingi	Ukaribu wa Tabaka Nyingi
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2013–2014	2013–2014
Mwanzilishi	De Domenico, M.; Kivelä, M.; Arenas, A. et al.	Kivela, M. et al.; De Domenico, M. et al.
Aina	Centrality measure (multilayer extension)	Centrality measure for multilayer networks
Chanzo asilia	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.
Majina mbadala	MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality	multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality
Zinazohusiana	5	5
Muhtasari	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.