Comparar métodos

	Análisis de Redes Temporales Múltiplex	Detección Temporal de Comunidades
Campo	Análisis de redes	Análisis de redes
Familia	Machine learning	Machine learning
Año de origen	2012–2014	2010
Autor original	Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors)	Mucha, P. J. et al.
Tipo	Structural and dynamic network analysis	Network clustering algorithm
Fuente seminal	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.
Alias	TMNA, time-varying multiplex network analysis, dynamic multiplex network analysis, temporal multilayer network analysis	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Relacionados	5	6
Resumen	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.	Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.