手法を比較

	指向性固有ベクトル中心性	Directed PageRank
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Machine learning
提唱年	1972–1987	1998
提唱者	Bonacich, P.	Brin, S. & Page, L.
種類	Centrality measure (eigenvector-based, directed)	Iterative authority-scoring algorithm
原典	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.	Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Proceedings of the 7th International Conference on World Wide Web (WWW7), 107–117. Elsevier.
別名	directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality	PageRank, PR, Google PageRank, directed link analysis
関連	5	5
概要	Directed eigenvector centrality extends the classic eigenvector centrality to directed graphs by scoring each node according to the centrality of the nodes that point to it (in-direction) or that it points to (out-direction). A node earns a high score not merely by having many connections but by being connected to other highly central nodes, capturing asymmetric influence in citation networks, social hierarchies, and information flows.	Directed PageRank is a link-based authority scoring algorithm that assigns importance scores to nodes in a directed graph by iteratively redistributing rank through outgoing edges. Introduced by Brin and Page in 1998 as the backbone of Google Search, it measures not just how many in-links a node has but how authoritative the nodes pointing to it are.