方法对比

	贝叶斯PageRank	定向PageRank
领域	网络分析	网络分析
方法族	Machine learning	Machine learning
起源年份	1999 (PageRank); 2000s (Bayesian extension)	1998
提出者	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	Brin, S. & Page, L.
类型	Probabilistic centrality measure	Iterative authority-scoring algorithm
开创性文献	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.
别名	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	PageRank, PR, Google PageRank, directed link analysis
相关	6	5
摘要	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.