Сравнение методов

	Анализ диффузии в темпоральных сетях	Анализ мультиплексных сетей
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2012	2014
Автор метода	Holme, P. & Saramäki, J.	Kivela, M.; Boccaletti, S. et al.
Тип	Network analysis framework	Structural network model
Основополагающий источник	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.	Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.
Другие названия	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
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.	Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.