Comparer des méthodes

	Analyse pondérée des réseaux multiplexes	Détection pondérée de communautés
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2014	2004–2008
Auteur d'origine	Battiston, F.; Kivela, M. et al.	Newman, M. E. J.; Blondel et al.
Type	Network analysis framework	Graph clustering / community detection
Source fondatrice	Battiston, F., Nicosia, V., & Latora, V. (2014). Structural measures for multiplex networks. Physical Review E, 89(3), 032804.	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	WMNA, weighted multilayer network analysis, weighted multi-relational network analysis, multiplex weighted graph analysis	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Apparentées	5	6
Résumé	Weighted multiplex network analysis studies systems in which the same set of actors are connected through multiple types of relationships simultaneously, and each relationship carries a quantitative strength or frequency. By capturing both the variety and the intensity of ties across layers, it reveals patterns invisible to single-layer or unweighted network approaches.	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.