Comparer des méthodes

	Analyse de la diffusion sur réseaux temporels	Détection de communautés temporelles
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2012	2010
Auteur d'origine	Holme, P. & Saramäki, J.	Mucha, P. J. et al.
Type	Network analysis framework	Network clustering algorithm
Source fondatrice	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	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	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Apparentées	5	6
Résumé	Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss.	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.