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| Độ trung tâm giữa hai điểm theo thời gian (Temporal Betweenness Centrality)× | Phân tích khuếch tán mạng theo thời gian× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời | 2012 | 2012 |
| Người khởi xướng≠ | Kim, H. & Anderson, R.; Holme, P. & Saramäki, J. | Holme, P. & Saramäki, J. |
| Loại≠ | Centrality measure for temporal networks | Network analysis framework |
| Công trình gốc≠ | Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Tên gọi khác | TBC, time-varying betweenness centrality, dynamic betweenness centrality, time-respecting betweenness | TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | 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 Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss. |
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