Comparar métodos

	Detección Temporal de Comunidades	Análisis de Redes Temporales
Campo	Análisis de redes	Análisis de redes
Familia	Machine learning	Process / pipeline
Año de origen	2010	2012
Autor original	Mucha, P. J. et al.	Holme & Saramäki (2012) — seminal framework
Tipo	Network clustering algorithm	Dynamic graph analysis
Fuente seminal	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.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Alias	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Relacionados	6	3
Resumen	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.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.