Võrdle meetodeid

	Dünaamiline mooduluse analüüs	Ajaline kogukonnatuvastus
Valdkond	Võrgustikuanalüüs	Võrgustikuanalüüs
Perekond	Machine learning	Machine learning
Tekkeaasta	2010	2010
Looja	Mucha, P. J.; Porter, M. A.; and colleagues	Mucha, P. J. et al.
Tüüp	Community detection on temporal networks	Network clustering algorithm
Algallikas	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.
Rööpnimetused	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
Seotud	5	6
Kokkuvõte	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.