Comparer des méthodes

	Analyse de la Diffusion par Réseaux Bayésiens	Analyse de la diffusion sur réseaux temporels
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2010s	2012
Auteur d'origine	Gomez Rodriguez, M.; Leskovec, J.; and related network science community	Holme, P. & Saramäki, J.
Type	Probabilistic inference on network spreading processes	Network analysis framework
Source fondatrice	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.	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Alias	Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks
Apparentées	5	5
Résumé	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.	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.