Linganisha mbinu

	Uchanganuzi wa Grafu za Maarifa za Muda	Uchanganuzi wa Mitandao ya Kijamii ya Muda
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2017–2018	2000s–2010s
Mwanzilishi	Trivedi, R. et al.; Dasgupta, S. S. et al.	Moody, J.; Holme, P.; Saramäki, J.
Aina	Temporal graph embedding and reasoning	Longitudinal network analysis
Chanzo asilia	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.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Majina mbadala	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
Zinazohusiana	5	4
Muhtasari	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.