Сравнение на методи

	Анализ на темпорални мултиплексни мрежи	Dynamic Community Detection
Област	Мрежови анализ	Мрежови анализ
Семейство	Machine learning	Machine learning
Година на възникване	2012–2014	2010 (key formalization); earlier work 2002–2009
Създател	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
Основополагащ източник	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.
Други 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
Свързани	5	5
Резюме	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.