Сравнение на методи

	Анализ на темпорална мрежова дифузия	Времеви анализ на социални мрежи
Област	Мрежови анализ	Мрежови анализ
Семейство	Machine learning	Machine learning
Година на възникване	2012	2000s–2010s
Създател	Holme, P. & Saramäki, J.	Moody, J.; Holme, P.; Saramäki, J.
Тип	Network analysis framework	Longitudinal network analysis
Основополагащ източник	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.
Други названия	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
Свързани	5	4
Резюме	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.