Methoden vergelijken

	Analyse van Temporele Netwerkdiffusie	Multiplex Netwerkanalyse
Vakgebied	Netwerkanalyse	Netwerkanalyse
Familie	Machine learning	Machine learning
Jaar van ontstaan	2012	2014
Grondlegger	Holme, P. & Saramäki, J.	Kivela, M.; Boccaletti, S. et al.
Type	Network analysis framework	Structural network model
Oorspronkelijke bron	Holme, P. & Saramäki, 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	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Verwant	5	6
Samenvatting	Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss.	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.