Methoden vergelijken

	Gewogen temporele netwerkanalyse	Multiplex Netwerkanalyse
Vakgebied	Netwerkanalyse	Netwerkanalyse
Familie	Machine learning	Machine learning
Jaar van ontstaan	2004–2012	2014
Grondlegger	Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)	Kivela, M.; Boccaletti, S. et al.
Type	Network analysis technique	Structural network model
Oorspronkelijke bron	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	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.
Aliassen	WTNA, weighted time-varying network analysis, weighted dynamic network analysis, weighted evolving network analysis	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Verwant	6	6
Samenvatting	Weighted temporal network analysis studies networks whose edges carry numerical weights — representing interaction strength, frequency, or intensity — and whose structure changes over time. It combines the time-varying perspective of temporal network analysis with the quantitative precision of weighted graph metrics, revealing not only when connections exist but how strong they are at each moment.	Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.