방법 비교

	Directed Closeness Centrality	방향성 고유벡터 중심성
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	1979–1994	1972–1987
창시자	Freeman, L. C.; Wasserman, S. & Faust, K.	Bonacich, P.
유형	Centrality measure	Centrality measure (eigenvector-based, directed)
원전	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4	Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.
별칭	directed closeness, in-closeness centrality, out-closeness centrality, directional closeness	directed EC, asymmetric eigenvector centrality, right eigenvector centrality, left eigenvector centrality
관련	5	5
요약	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 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.