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| Kemeradulan Rentasan Masa× | Sentraliti Kehampiran Temporal× | |
|---|---|---|
| Bidang | Analisis Rangkaian | Analisis Rangkaian |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2012 | 2011 |
| Pengasas≠ | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. | Pan, R. K. & Saramaki, J. |
| Jenis≠ | Centrality measure for temporal networks | Centrality measure (temporal) |
| Sumber perintis≠ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105. DOI ↗ |
| Alias | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality |
| Berkaitan | 6 | 6 |
| Ringkasan≠ | Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot. | 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. |
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