Сравнение методов

	Многослойная центральность по посредничеству	Многослойная степень центральности
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2013–2014	2013–2014
Автор метода	De Domenico, M.; Kivelä, M.; Arenas, A. et al.	Kivelä, M.; De Domenico, M. et al.
Тип	Centrality measure (multilayer extension)	Centrality measure for multilayer networks
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.	Kivelä, 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	multilayer degree, multiplex degree centrality, overlapping-layer degree centrality, MDC
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.	Multilayer degree centrality extends the classic degree centrality measure to networks composed of multiple layers — such as networks representing different types of social ties, communication channels, or relationship contexts simultaneously. It quantifies how many connections a node has across one or all layers, revealing nodes that are influential not just in a single context but across the entire multi-relational structure.