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	Bayesian PageRank	Bayesiansk gemenskapsdetektion
Ämnesområde	Nätverksanalys	Nätverksanalys
Familj	Machine learning	Machine learning
Ursprungsår	1999 (PageRank); 2000s (Bayesian extension)	2001–2014
Upphovsperson	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.
Typ	Probabilistic centrality measure	Probabilistic generative model / inference
Ursprungskälla	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.
Alias	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	Bayesian graph clustering, probabilistic community detection, Bayesian stochastic block model community detection, Bayesian network partitioning
Närliggande	6	5
Sammanfattning	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.