Сравнение методов

	Многослойная центральность по близости	Многослойная центральность по посредничеству
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2013–2014	2013–2014
Автор метода	Kivela, M. et al.; De Domenico, M. et al.	De Domenico, M.; Kivelä, M.; Arenas, A. et al.
Тип	Centrality measure for multilayer networks	Centrality measure (multilayer extension)
Основополагающий источник	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.	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.
Другие названия	multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality	MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality
Связанные	5	5
Сводка	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.	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.