Сравнение методов

	Многослойная центральность по близости	Обнаружение многослойных сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2013–2014	2010–2014
Автор метода	Kivela, M. et al.; De Domenico, M. et al.	Mucha, P. J. et al.; Kivela, M. et al.
Тип	Centrality measure for multilayer networks	Community detection algorithm 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.	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.
Другие названия	multilayer closeness, multi-layer closeness centrality, MLC, interlayer closeness centrality	multilayer clustering, multiplex community detection, cross-layer community detection, MCD
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 community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss.