Salīdzināt metodes

	Bayesiešu PageRank	Beijesa tīkla difūzijas analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	1999 (PageRank); 2000s (Bayesian extension)	2010s
Autors	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	Gomez Rodriguez, M.; Leskovec, J.; and related network science community
Tips	Probabilistic centrality measure	Probabilistic inference on network spreading processes
Pirmavots	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.	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.
Citi nosaukumi	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	Bayesian diffusion model, probabilistic network diffusion, Bayesian spreading process inference, BNDA
Saistītās	6	5
Kopsavilkums	Bayesian PageRank extends the classic PageRank algorithm by embedding it within a Bayesian probabilistic framework. Instead of returning a single deterministic rank score for each node, it quantifies uncertainty over rank estimates — particularly valuable when the network is incomplete, noisy, or observed with error. It is used in web analysis, citation networks, and social network research where rank uncertainty matters.	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.