Comparer des méthodes

	Détection de communautés temporelles	Analyse de modularité
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2010	2004
Auteur d'origine	Mucha, P. J. et al.	Newman, M. E. J. & Girvan, M.
Type	Network clustering algorithm	Community detection / graph partitioning
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.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Alias	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Apparentées	6	5
Résumé	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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.