Salīdzināt metodes

	Svērta multipleksu tīklu analīze	Svērtas tīklu difūzijas analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2014	2004
Autors	Battiston, F.; Kivela, M. et al.	Barrat, A.; Newman, M. E. J.
Tips	Network analysis framework	Network diffusion model
Pirmavots	Battiston, F., Nicosia, V., & Latora, V. (2014). Structural measures for multiplex networks. Physical Review E, 89(3), 032804.	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.
Citi nosaukumi	WMNA, weighted multilayer network analysis, weighted multi-relational network analysis, multiplex weighted graph analysis	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion
Saistītās	5	6
Kopsavilkums	Weighted multiplex network analysis studies systems in which the same set of actors are connected through multiple types of relationships simultaneously, and each relationship carries a quantitative strength or frequency. By capturing both the variety and the intensity of ties across layers, it reveals patterns invisible to single-layer or unweighted network approaches.	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.