方法对比

	贝叶斯PageRank	多层 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)	2015
提出者	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.
类型	Probabilistic centrality measure	Centrality measure (random-walk-based)
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.	De Domenico, M., Sole-Ribalta, A., Omodei, E., Gomez, S., & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6, 6868.
别名	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank
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.	Multilayer PageRank extends the classic PageRank random-walk centrality to networks that contain multiple interconnected layers — such as a social network where people are connected simultaneously via friendship, professional ties, and online platforms. By allowing a virtual walker to jump both within and across layers, the algorithm identifies nodes that are influential across the entire multilayer structure, not just within any single layer.