Сравнение методов

	Взвешенный анализ временных сетей	Взвешенный анализ социальных сетей
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2004–2012	2004–2010
Автор метода	Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)	Barrat, A.; Opsahl, T. et al.
Тип	Network analysis technique	Network analysis framework
Основополагающий источник	Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.
Другие названия	WTNA, weighted time-varying network analysis, weighted dynamic network analysis, weighted evolving network analysis	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis
Связанные	6	6
Сводка	Weighted temporal network analysis studies networks whose edges carry numerical weights — representing interaction strength, frequency, or intensity — and whose structure changes over time. It combines the time-varying perspective of temporal network analysis with the quantitative precision of weighted graph metrics, revealing not only when connections exist but how strong they are at each moment.	Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships.