Comparer des méthodes

	Détection pondérée de communautés	Analyse de modularité pondérée
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2004–2008	2004
Auteur d'origine	Newman, M. E. J.; Blondel et al.	Newman, M. E. J.
Type	Graph clustering / community detection	Community structure optimization on weighted graphs
Source fondatrice	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.	Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.
Alias	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD	weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity
Apparentées	6	5
Résumé	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.	Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully.