Salīdzināt metodes

	Beijesa tīkla difūzijas analīze	Laika tīklu difūzijas analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2010s	2012
Autors	Gomez Rodriguez, M.; Leskovec, J.; and related network science community	Holme, P. & Saramäki, J.
Tips	Probabilistic inference on network spreading processes	Network analysis framework
Pirmavots	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.
Citi nosaukumi	Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks
Saistītās	5	5
Kopsavilkums	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.