방법 비교

	Bayesian PageRank	방향성 페이지랭크
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	1999 (PageRank); 2000s (Bayesian extension)	1998
창시자	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	Brin, S. & Page, L.
유형	Probabilistic centrality measure	Iterative authority-scoring algorithm
원전	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.	Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Proceedings of the 7th International Conference on World Wide Web (WWW7), 107–117. Elsevier.
별칭	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	PageRank, PR, Google PageRank, directed link analysis
관련	6	5
요약	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.	Directed PageRank is a link-based authority scoring algorithm that assigns importance scores to nodes in a directed graph by iteratively redistributing rank through outgoing edges. Introduced by Brin and Page in 1998 as the backbone of Google Search, it measures not just how many in-links a node has but how authoritative the nodes pointing to it are.