Comparer des méthodes

	Analyse de la Diffusion par Réseaux Bayésiens	Modèle bayésien de graphe aléatoire exponentiel
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2010s	2011
Auteur d'origine	Gomez Rodriguez, M.; Leskovec, J.; and related network science community	Caimo, A., & Friel, N.
Type	Probabilistic inference on network spreading processes	Bayesian statistical model for networks
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.	Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55.
Alias	Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA	Bayesian ERGM, Bayesian p-star model, Bayesian p* model, BERGM
Apparentées	5	4
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.	The Bayesian Exponential Random Graph Model (Bayesian ERGM or BERGM) extends the classical ERGM framework by placing prior distributions over the model parameters and using Markov chain Monte Carlo methods to obtain full posterior distributions. Introduced by Caimo and Friel (2011), it allows researchers to quantify parameter uncertainty and incorporate prior knowledge when modelling the structural features of social and other complex networks.