방법 비교

	Bayesian PageRank	고유벡터 중심성
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	1999 (PageRank); 2000s (Bayesian extension)	1972
창시자	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	Bonacich, P.
유형	Probabilistic centrality measure	Centrality measure
원전	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.	Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
별칭	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	eigenvector centrality, EC, Bonacich centrality, power centrality
관련	6	6
요약	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.	Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network.