Сравнение методов

	Анализ диффузии в темпоральных сетях	Выявление временных сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2012	2010
Автор метода	Holme, P. & Saramäki, J.	Mucha, P. J. et al.
Тип	Network analysis framework	Network clustering algorithm
Основополагающий источник	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.
Другие названия	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Связанные	5	6
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 community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.