Methoden vergelijken

	Gewogen temporele netwerkanalyse	Netwerkdiffusieanalyse
Vakgebied	Netwerkanalyse	Netwerkanalyse
Familie	Machine learning	Machine learning
Jaar van ontstaan	2004–2012	1927 (epidemic roots); network formalization 1990s–2000s
Grondlegger	Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)	Kermack, W. O. & McKendrick, A. G.
Type	Network analysis technique	Simulation / analytical model
Oorspronkelijke bron	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.
Aliassen	WTNA, weighted time-varying network analysis, weighted dynamic network analysis, weighted evolving network analysis	diffusion on networks, information diffusion, contagion spreading model, network propagation model
Verwant	6	5
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.	Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.