Сравнение методов

	Многослойная центральность по близости	Центральность по близости
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2013–2014	1950 (formalized 1979)
Автор метода	Kivela, M. et al.; De Domenico, M. et al.	Bavelas, A.; formalized by Freeman, L. C.
Тип	Centrality measure for multilayer networks	Node-level centrality index
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.	Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Другие названия	multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality
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.	Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts.