So sánh phương pháp

	Phân tích khuếch tán mạng có trọng số	Phát hiện cộng đồng có trọng số
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	2004	2004–2008
Người khởi xướng	Barrat, A.; Newman, M. E. J.	Newman, M. E. J.; Blondel et al.
Loại	Network diffusion model	Graph clustering / community detection
Công trình gốc	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.
Tên gọi khác	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Liên quan	6	6
Tóm tắt	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.