विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| निर्देशित निकटता केन्द्रीयता× | निकटता केंद्रीयता (Closeness Centrality)× | |
|---|---|---|
| क्षेत्र | नेटवर्क विश्लेषण | नेटवर्क विश्लेषण |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 1979–1994 | 1950 (formalized 1979) |
| प्रवर्तक≠ | Freeman, L. C.; Wasserman, S. & Faust, K. | Bavelas, A.; formalized by Freeman, L. C. |
| प्रकार≠ | Centrality measure | Node-level centrality index |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम | directed closeness, in-closeness centrality, out-closeness centrality, directional closeness | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| संबंधित≠ | 5 | 6 |
| सारांश≠ | 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. |
| ScholarGateडेटासेट ↗ |
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