Сравнение методов

	Многослойная центральность по близости	Многослойная степень центральности
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.	Kivelä, M.; De Domenico, M. et al.
Тип	Centrality measure for multilayer networks	Centrality measure for multilayer networks
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.	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	multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality	multilayer degree, multiplex degree centrality, overlapping-layer degree centrality, MDC
Связанные	5	6
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 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.