Sammenlign metoder

	Bayesiansk PageRank	Multilayer PageRank
Fagområde	Netværksanalyse	Netværksanalyse
Familie	Machine learning	Machine learning
Oprindelsesår	1999 (PageRank); 2000s (Bayesian extension)	2015
Ophavsperson	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.
Type	Probabilistic centrality measure	Centrality measure (random-walk-based)
Oprindelig kilde	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.
Aliasser	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	multiplex PageRank, layer-coupled PageRank, multilayer random walk centrality, MuxRank
Relaterede	6	5
Resumé	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.