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| Phân tích Đồ thị Tri thức 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≠ | 2017–2018 | 2000s–2010s |
| Người khởi xướng≠ | Trivedi, R. et al.; Dasgupta, S. S. et al. | Moody, J.; Holme, P.; Saramäki, J. |
| Loại≠ | Temporal graph embedding and reasoning | Longitudinal network analysis |
| 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 | TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA |
| Liên quan≠ | 5 | 4 |
| 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 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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