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| Độ tập trung cận kề thời gian× | Phân tích mạng xã hội theo thời gian× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2011 | 2000s–2010s |
| Người khởi xướng≠ | Pan, R. K. & Saramaki, J. | Moody, J.; Holme, P.; Saramäki, J. |
| Loại≠ | Centrality measure (temporal) | Longitudinal network analysis |
| Công trình gốc≠ | Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105. DOI ↗ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Tên gọi khác | time-varying closeness centrality, dynamic closeness centrality, TCC, temporal reachability-based centrality | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Liên quan≠ | 6 | 4 |
| Tóm tắt≠ | 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. | Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time. |
| ScholarGateBộ dữ liệu ↗ |
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