So sánh phương pháp

	PageRank Bayes	PageRank Đa Lớp
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	1999 (PageRank); 2000s (Bayesian extension)	2015
Người khởi xướng	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.
Loại	Probabilistic centrality measure	Centrality measure (random-walk-based)
Công trình gốc	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.
Tên gọi khác	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank
Liên quan	6	5
Tóm tắt	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.