Salīdzināt metodes

	Daudzslāņu laika tīklu analīze	Daudzslāņu kopienu noteikšana
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2012–2014	2010–2014
Autors	Kivela, M. et al.; Holme, P. & Saramaki, J.	Mucha, P. J. et al.; Kivela, M. et al.
Tips	Network analysis framework	Community detection algorithm for multilayer networks
Pirmavots	Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.	Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.
Citi nosaukumi	MTNA, temporal multilayer network analysis, time-varying multilayer network analysis, dynamic multilayer network analysis	multilayer clustering, multiplex community detection, cross-layer community detection, MCD
Saistītās	4	5
Kopsavilkums	Multilayer temporal network analysis studies relational systems in which nodes interact through multiple distinct types of ties that all evolve over time. By modeling each relationship type as a separate layer and tracking how those layers change across time snapshots, the method reveals how cross-layer dynamics and temporal patterns jointly shape information flow, influence spread, and community structure.	Multilayer community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss.