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| Directed Closeness Centrality× | 근접 중심성× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1979–1994 | 1950 (formalized 1979) |
| 창시자≠ | Freeman, L. C.; Wasserman, S. & Faust, K. | Bavelas, A.; formalized by Freeman, L. C. |
| 유형≠ | Centrality measure | Node-level centrality index |
| 원전≠ | 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. DOI ↗ |
| 별칭 | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| 관련≠ | 5 | 6 |
| 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. | 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. |
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