Comparer des méthodes

	Détection dirigée de communautés	Détection pondérée de communautés
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2008	2004–2008
Auteur d'origine	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.	Newman, M. E. J.; Blondel et al.
Type	Graph partitioning / modularity optimization	Graph clustering / community detection
Source fondatrice	Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	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	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Apparentées	6	6
Résumé	Directed community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways.	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.