Сравнение методов

	Многослойная степень центральности	Многослойная центральность по посредничеству
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2013–2014	2013–2014
Автор метода	Kivelä, M.; 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	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.	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 degree, multiplex degree centrality, overlapping-layer degree centrality, MDC	MBC, multilayer geodesic betweenness, tensorial betweenness centrality, interlayer betweenness centrality
Связанные	6	5
Сводка	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.	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.