Comparer des méthodes

	Analyse de modularité dynamique	Détection de communautés temporelles
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2010	2010
Auteur d'origine	Mucha, P. J.; Porter, M. A.; and colleagues	Mucha, P. J. et al.
Type	Community detection on temporal networks	Network clustering algorithm
Source fondatrice	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.	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	dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Apparentées	5	6
Résumé	Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data.	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.