手法を比較

	Directed Closeness Centrality	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	1979–1994	1998
提唱者	Freeman, L. C.; Wasserman, S. & Faust, K.	Brin, S. & Page, L.
種類	Centrality measure	Iterative authority-scoring algorithm
原典	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4	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 closeness, in-closeness centrality, out-closeness centrality, directional closeness	PageRank, PR, Google PageRank, directed link analysis
関連	5	5
概要	Directed closeness centrality extends the classical closeness measure to directed networks by separately quantifying how quickly a node can be reached by others (in-closeness) and how quickly it can reach all others (out-closeness). It is a foundational node-level metric in social network analysis and graph theory, used wherever link direction conveys meaningful asymmetry such as citation flows, information cascades, or authority hierarchies.	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.