قارن الطرق

	ترتيب الصفحة البايزي	PageRank متعدد الطبقات
المجال	تحليل الشبكات	تحليل الشبكات
العائلة	Machine learning	Machine learning
سنة النشأة	1999 (PageRank); 2000s (Bayesian extension)	2015
صاحب الطريقة	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.
النوع	Probabilistic centrality measure	Centrality measure (random-walk-based)
المصدر التأسيسي	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.
الأسماء البديلة	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank
ذات صلة	6	5
الملخص	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.