Сравнение методов

	Анализ диффузии в темпоральных сетях	Временной анализ социальных сетей
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2012	2000s–2010s
Автор метода	Holme, P. & Saramäki, J.	Moody, J.; Holme, P.; Saramäki, J.
Тип	Network analysis framework	Longitudinal network analysis
Основополагающий источник	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
Другие названия	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
Связанные	5	4
Сводка	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.	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.