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Analiza vremenskih grafova znanja×Vremenska analiza društvenih mreža×
PodručjeAnaliza mrežaAnaliza mreža
ObiteljMachine learningMachine learning
Godina nastanka2017–20182000s–2010s
TvoracTrivedi, R. et al.; Dasgupta, S. S. et al.Moody, J.; Holme, P.; Saramäki, J.
VrstaTemporal graph embedding and reasoningLongitudinal network analysis
Temeljni izvorTrivedi, 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 ↗
Drugi naziviTKG analysis, temporal KG analysis, dynamic knowledge graph analysis, time-aware knowledge graph analysisTSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
Srodne54
SažetakTemporal 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.
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ScholarGateUsporedite metode: Temporal Knowledge Graph Analysis · Temporal Social Network Analysis. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare