Bandingkan metode

	Analisis Difusi Jaringan Berbobot	Analisis Jaringan Multiplex
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2004	2014
Pencetus	Barrat, A.; Newman, M. E. J.	Kivela, M.; Boccaletti, S. et al.
Tipe	Network diffusion model	Structural network model
Sumber perintis	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.
Alias	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
Terkait	6	6
Ringkasan	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.