Compara mètodes

	Anàlisi de Modularitat Dinàmica	Detecció de Comunitats Temporals
Camp	Anàlisi de xarxes	Anàlisi de xarxes
Família	Machine learning	Machine learning
Any d'origen	2010	2010
Autor original	Mucha, P. J.; Porter, M. A.; and colleagues	Mucha, P. J. et al.
Tipus	Community detection on temporal networks	Network clustering algorithm
Font 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.	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.
Àlies	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
Relacionats	5	6
Resum	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.