Сравнение методов

	Центральность по направленной близости	Центральность по близости
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	1979–1994	1950 (formalized 1979)
Автор метода	Freeman, L. C.; Wasserman, S. & Faust, K.	Bavelas, A.; formalized by Freeman, L. C.
Тип	Centrality measure	Node-level centrality index
Основополагающий источник	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4	Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Другие названия	directed closeness, in-closeness centrality, out-closeness centrality, directional closeness	closeness, farness-based centrality, geodesic closeness, normalized closeness centrality
Связанные	5	6
Сводка	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.	Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts.