Comparer des méthodes

	Détection dirigée de communautés	Analyse de modularité
Domaine	Analyse de réseaux	Analyse de réseaux
Famille	Machine learning	Machine learning
Année d'origine	2008	2004
Auteur d'origine	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.	Newman, M. E. J. & Girvan, M.
Type	Graph partitioning / modularity optimization	Community detection / graph partitioning
Source fondatrice	Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Alias	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.