Salīdzināt metodes

	Laika tīklu difūzijas analīze	Daudzslāņu tīklu analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2012	2014
Autors	Holme, P. & Saramäki, J.	Kivela, M.; Boccaletti, S. et al.
Tips	Network analysis framework	Structural network model
Pirmavots	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.
Citi nosaukumi	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Saistītās	5	6
Kopsavilkums	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.