Linganisha mbinu

	Uchanganuzi wa Kati wa Tabaka Nyingi	Uchanganuzi wa Mitandao Mingi (Multiplex Network Analysis)
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2013–2014	2014
Mwanzilishi	De Domenico, M.; Kivelä, M.; Arenas, A. et al.	Kivela, M.; Boccaletti, S. et al.
Aina	Centrality measure (multilayer extension)	Structural network model
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	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Zinazohusiana	5	6
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.	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.