Módszerek összehasonlítása

	Bayesian PageRank	Bayes-féle közösségdetektálás
Tudományterület	Hálózatelemzés	Hálózatelemzés
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	1999 (PageRank); 2000s (Bayesian extension)	2001–2014
Megalkotó	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.
Típus	Probabilistic centrality measure	Probabilistic generative model / inference
Alapmű	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.	Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.
Alternatív nevek	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning
Kapcsolódó	6	5
Összefoglaló	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 community detection infers latent group structure in networks by treating community membership as unobserved variables and using Bayesian inference — typically via Markov chain Monte Carlo or variational methods — to compute a posterior distribution over all plausible partitions. Unlike modularity optimisation, it selects the number of communities from data and provides principled uncertainty estimates for every node assignment.