Сравнение методов

	Анализ многослойных временных сетей	Обнаружение многослойных сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2012–2014	2010–2014
Автор метода	Kivela, M. et al.; Holme, P. & Saramaki, J.	Mucha, P. J. et al.; Kivela, M. et al.
Тип	Network analysis framework	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	MTNA, temporal multilayer network analysis, time-varying multilayer network analysis, dynamic multilayer network analysis	multilayer clustering, multiplex community detection, cross-layer community detection, MCD
Связанные	4	5
Сводка	Multilayer temporal network analysis studies relational systems in which nodes interact through multiple distinct types of ties that all evolve over time. By modeling each relationship type as a separate layer and tracking how those layers change across time snapshots, the method reveals how cross-layer dynamics and temporal patterns jointly shape information flow, influence spread, and community structure.	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.