Сравнение методов

	Многослойная центральность по посредничеству	Анализ мультиплексных сетей
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2013–2014	2014
Автор метода	De Domenico, M.; Kivelä, M.; Arenas, A. et al.	Kivela, M.; Boccaletti, S. et al.
Тип	Centrality measure (multilayer extension)	Structural network model
Основополагающий источник	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.
Другие названия	MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Связанные	5	6
Сводка	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.