Salīdzināt metodes

	Temporālā kopienu noteikšana	Laika tīklu analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2010	2012
Autors	Mucha, P. J. et al.	Holme & Saramäki (2012) — seminal framework
Tips	Network clustering algorithm	Dynamic graph analysis
Pirmavots	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.
Citi nosaukumi	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)
Saistītās	6	3
Kopsavilkums	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.