विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| कालिक निकटता केंद्रीयता× | निकटता केंद्रीयता (Closeness Centrality)× | |
|---|---|---|
| क्षेत्र | नेटवर्क विश्लेषण | नेटवर्क विश्लेषण |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2011 | 1950 (formalized 1979) |
| प्रवर्तक≠ | Pan, R. K. & Saramaki, J. | Bavelas, A.; formalized by Freeman, L. C. |
| प्रकार≠ | Centrality measure (temporal) | Node-level centrality index |
| मौलिक स्रोत≠ | Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| उपनाम | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| संबंधित | 6 | 6 |
| सारांश≠ | Temporal closeness centrality extends the classical closeness measure to time-varying networks by replacing static shortest paths with time-respecting (foremost) paths. It quantifies how quickly a node can reach all other nodes when interactions occur at specific moments in time, giving a more realistic picture of information flow, disease spread, and influence in dynamic systems. | 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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