השוואת שיטות

	PageRank בייסיאני	PageRank רב-שכבתי
תחום	ניתוח רשתות	ניתוח רשתות
משפחה	Machine learning	Machine learning
שנת המקור	1999 (PageRank); 2000s (Bayesian extension)	2015
הוגה השיטה	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.
סוג	Probabilistic centrality measure	Centrality measure (random-walk-based)
מקור מכונן	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.
כינויים	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank
קשורות	6	5
תקציר	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.