Methoden vergelijken

	Gerichte Closeness Centraliteit	Gerichte Netwerkanalyse
Vakgebied	Netwerkanalyse	Netwerkanalyse
Familie	Machine learning	Machine learning
Jaar van ontstaan	1979–1994	1994
Grondlegger	Freeman, L. C.; Wasserman, S. & Faust, K.	Wasserman, S. & Faust, K.
Type	Centrality measure	Structural analysis of directed graphs
Oorspronkelijke bron	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Aliassen	directed closeness, in-closeness centrality, out-closeness centrality, directional closeness	directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis
Verwant	5	5
Samenvatting	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 Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades.