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| Độ tập trung bậc theo thời gian× | Tính trung tâm eigenvector thời gian× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2011–2012 | 2011-2017 |
| Người khởi xướng≠ | Holme, P.; Saramaki, J.; Kim, H.; Anderson, R. | Grindrod, P.; Higham, D. J.; Taylor, D. et al. |
| Loại≠ | Centrality measure (temporal extension) | Centrality measure for temporal networks |
| Công trình gốc≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120. DOI ↗ |
| Tên gọi khác | time-varying degree centrality, dynamic degree centrality, temporal node degree, TDC | dynamic eigenvector centrality, time-varying eigenvector centrality, TEC, temporal communicability centrality |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window. | Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network. |
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