Comparer des méthodes

	Détection dirigée de communautés	Analyse des réseaux sociaux orientés
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2008	1994
Auteur d'origine	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.	Wasserman, S. & Faust, K.
Type	Graph partitioning / modularity optimization	Structural analysis of directed graphs
Source fondatrice	Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Alias	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning	directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis
Apparentées	6	5
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.	Directed Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades.