Comparer des méthodes

	Analyse de modularité dynamique	Analyse de réseaux multiplex
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2010	2014
Auteur d'origine	Mucha, P. J.; Porter, M. A.; and colleagues	Kivela, M.; Boccaletti, S. et al.
Type	Community detection on temporal networks	Structural network model
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.	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.
Alias	dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
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.	Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.