Salīdzināt metodes

	Svērtas tīklu difūzijas analīze	Daudzslāņu tīklu analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2004	2014
Autors	Barrat, A.; Newman, M. E. J.	Kivela, M.; Boccaletti, S. et al.
Tips	Network diffusion model	Structural network model
Pirmavots	Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.	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	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Saistītās	6	6
Kopsavilkums	Weighted Network Diffusion Analysis models how information, influence, disease, or resources spread through a network whose edges carry quantitative strength values. By letting tie weights govern transition probabilities, the method produces more realistic spreading dynamics than binary-edge diffusion, revealing which high-traffic pathways dominate propagation in social, biological, and information networks.	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.