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| Phân tích Đồ thị Tri thức Thời gian× | 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≠ | 2017–2018 | 2012 |
| Người khởi xướng≠ | Trivedi, R. et al.; Dasgupta, S. S. et al. | Holme, P. & Saramäki, J. |
| Loại≠ | Temporal graph embedding and reasoning | Network analysis framework |
| Công trình gốc≠ | Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017). Know-Evolve: Deep temporal reasoning for dynamic knowledge graphs. Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 3462–3471. link ↗ | Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ |
| Tên gọi khác | TKG analysis, temporal KG analysis, dynamic knowledge graph analysis, time-aware knowledge graph analysis | TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks |
| Liên quan | 5 | 5 |
| Tóm tắt≠ | Temporal Knowledge Graph Analysis extends standard knowledge graph methods to data where facts and relationships carry timestamps or validity intervals. It enables reasoning about how entities and relations evolve over time, supporting tasks such as link prediction for future facts, temporal relation classification, and event forecasting in dynamic relational data. | 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. |
| ScholarGateBộ dữ liệu ↗ |
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