方法对比

	加权网络扩散分析	加权社区检测
领域	网络分析	网络分析
方法族	Machine learning	Machine learning
起源年份	2004	2004–2008
提出者	Barrat, A.; Newman, M. E. J.	Newman, M. E. J.; Blondel et al.
类型	Network diffusion model	Graph clustering / community detection
开创性文献	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.	Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
别名	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
相关	6	6
摘要	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.	Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation.