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	Analisi Bayesiana della Diffusione su Reti	Analisi di Diffusione su Reti Temporali
Campo	Analisi delle reti	Analisi delle reti
Famiglia	Machine learning	Machine learning
Anno di origine	2010s	2012
Ideatore	Gomez Rodriguez, M.; Leskovec, J.; and related network science community	Holme, P. & Saramäki, J.
Tipo	Probabilistic inference on network spreading processes	Network analysis framework
Fonte seminale	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
Correlati	5	5
Sintesi	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.