Machine learningNetwork science

Directed Closeness Centrality

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.

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出典

  1. Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38269-4
  2. Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239. DOI: 10.1016/0378-8733(78)90021-7

このページの引用方法

ScholarGate. (2026, June 3). Directed Closeness Centrality (In-closeness and Out-closeness on Directed Graphs). ScholarGate. https://scholargate.app/ja/network-analysis/directed-closeness-centrality

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この手法を参照する項目

ScholarGateDirected Closeness Centrality (Directed Closeness Centrality (In-closeness and Out-closeness on Directed Graphs)). 2026-06-15に以下より取得 https://scholargate.app/ja/network-analysis/directed-closeness-centrality · データセット: https://doi.org/10.5281/zenodo.20539026