Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Centralitatea de proximitate temporală× | Centralitate de Apropiere× | |
|---|---|---|
| Domeniu | Analiza rețelelor | Analiza rețelelor |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2011 | 1950 (formalized 1979) |
| Autorul original≠ | Pan, R. K. & Saramaki, J. | Bavelas, A.; formalized by Freeman, L. C. |
| Tip≠ | Centrality measure (temporal) | Node-level centrality index |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality |
| Înrudite | 6 | 6 |
| Rezumat≠ | 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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