Сравнение методов

	Анализ темпоральных мультиплексных сетей	Динамическое обнаружение сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2012–2014	2010 (key formalization); earlier work 2002–2009
Автор метода	Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors)	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)
Тип	Structural and dynamic network analysis	Graph clustering / community discovery
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.	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.
Другие названия	TMNA, time-varying multiplex network analysis, dynamic multiplex network analysis, temporal multilayer network analysis	DCD, temporal community detection, evolving community detection, dynamic graph clustering
Связанные	5	5
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.	Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.