방법 비교

	가중 네트워크 확산 분석	다중망 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2004	2014
창시자	Barrat, A.; Newman, M. E. J.	Kivela, M.; Boccaletti, S. et al.
유형	Network diffusion model	Structural network model
원전	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.
별칭	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA
관련	6	6
요약	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.