Methoden vergelijken

	Analyse van Multilayer-socialenetwerken	Temporale Netwerkanalyse
Vakgebied	Netwerkanalyse	Netwerkanalyse
Familie	Machine learning	Process / pipeline
Jaar van ontstaan	2014	2012
Grondlegger	Kivela, M.; Boccaletti, S. et al.	Holme & Saramäki (2012) — seminal framework
Type	Structural network analysis framework	Dynamic graph analysis
Oorspronkelijke bron	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.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Aliassen	MSNA, multiplex network analysis, multilayer network analysis, interconnected network analysis	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Verwant	6	3
Samenvatting	Multilayer social network analysis extends classical single-layer network methods to settings where actors are connected through multiple, distinct types of ties — such as friendship, professional collaboration, and online interaction — simultaneously. By modeling each type of relationship as a separate layer and explicitly representing connections across layers, it captures structural complexity that a single aggregated network would hide.	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.