Methoden vergelijken

	Bayesiaanse Netwerkdiffusieanalyse	Netwerkdiffusieanalyse
Vakgebied	Netwerkanalyse	Netwerkanalyse
Familie	Machine learning	Machine learning
Jaar van ontstaan	2010s	1927 (epidemic roots); network formalization 1990s–2000s
Grondlegger	Gomez Rodriguez, M.; Leskovec, J.; and related network science community	Kermack, W. O. & McKendrick, A. G.
Type	Probabilistic inference on network spreading processes	Simulation / analytical model
Oorspronkelijke bron	Gomez Rodriguez, M., Leskovec, J., & Scholkopf, B. (2012). Structure and Dynamics of Information Pathways in Online Media. Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM), 23–32.	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	Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA	diffusion on networks, information diffusion, contagion spreading model, network propagation model
Verwant	5	5
Samenvatting	Bayesian Network Diffusion Analysis applies Bayesian probabilistic inference to the study of how information, diseases, behaviors, or innovations propagate through a network. By placing priors over diffusion parameters and updating them with observed cascade data, it quantifies transmission rates, identifies influential spreaders, reconstructs latent propagation pathways, and provides full uncertainty estimates — all within a principled statistical framework.	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.