Сравнение методов

	Анализ темпоральных мультиплексных сетей	Анализ мультиплексных сетей
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2012–2014	2014
Автор метода	Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors)	Kivela, M.; Boccaletti, S. et al.
Тип	Structural and dynamic network analysis	Structural network model
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	TMNA, time-varying multiplex network analysis, dynamic multiplex network analysis, temporal multilayer network analysis	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Связанные	5	6
Сводка	Temporal multiplex network analysis studies relational systems in which actors are connected by multiple distinct types of relationships that all evolve over time. By simultaneously tracking layer heterogeneity and temporal dynamics, the method reveals how different interaction channels co-evolve, which actors hold persistent cross-layer influence, and how structural changes propagate across relationship types and time periods.	Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.