विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| भारित निकटता केन्द्रीयता× | निकटता केंद्रीयता (Closeness Centrality)× | |
|---|---|---|
| क्षेत्र | नेटवर्क विश्लेषण | नेटवर्क विश्लेषण |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2010 | 1950 (formalized 1979) |
| प्रवर्तक≠ | Opsahl, T.; Agneessens, F.; Skvoretz, J. | Bavelas, A.; formalized by Freeman, L. C. |
| प्रकार≠ | Centrality measure (network analysis) | Node-level centrality index |
| मौलिक स्रोत≠ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| उपनाम | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| संबंधित | 6 | 6 |
| सारांश≠ | Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart. | 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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