Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ukaribu wa Kati wa Nguvu (Dynamic Closeness Centrality)× | Ukaribu wa Kati (Closeness Centrality)× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Mitandao | Uchanganuzi wa Mitandao |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2010–2012 | 1950 (formalized 1979) |
| Mwanzilishi≠ | Tang, J. et al.; Holme, P. & Saramäki, J. | Bavelas, A.; formalized by Freeman, L. C. |
| Aina≠ | Centrality measure for temporal networks | Node-level centrality index |
| Chanzo asilia≠ | Tang, J., Musolesi, M., Mascolo, C., Latora, V. & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of the 3rd Workshop on Social Network Systems (SNS '10). ACM. DOI ↗ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ |
| Majina mbadala | temporal closeness centrality, time-varying closeness centrality, evolving network closeness, dynamic CC | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | Dynamic closeness centrality extends classic closeness centrality to temporal networks by computing shortest time-respecting paths — paths that traverse edges in chronological order — and averaging inverse distances across all time windows. It reveals which nodes are most efficiently reached within an evolving network, tracking how a node's centrality rises and falls as connections appear and disappear over time. | 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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