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	Detekcja społeczności w czasie	Analiza sieci czasowych
Dziedzina	Analiza sieci	Analiza sieci
Rodzina	Machine learning	Process / pipeline
Rok powstania	2010	2012
Twórca	Mucha, P. J. et al.	Holme & Saramäki (2012) — seminal framework
Typ	Network clustering algorithm	Dynamic graph analysis
Źródło pierwotne	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.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Inne nazwy	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Pokrewne	6	3
Podsumowanie	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.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.