Comparer des méthodes

	Analyse de la diffusion sur réseaux pondérés	Détection pondérée de communautés
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2004	2004–2008
Auteur d'origine	Barrat, A.; Newman, M. E. J.	Newman, M. E. J.; Blondel et al.
Type	Network diffusion model	Graph clustering / community detection
Source fondatrice	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.
Alias	WNDA, weighted diffusion process, edge-weighted spreading analysis, weighted information diffusion	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Apparentées	6	6
Résumé	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.