Comparar métodos

	Análisis de Modularidad Dirigida	Detección de Comunidades Dirigidas
Campo	Análisis de redes	Análisis de redes
Familia	Machine learning	Machine learning
Año de origen	2008	2008
Autor original	Leicht, E. A. & Newman, M. E. J.	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.
Tipo	Community detection / graph partitioning	Graph partitioning / modularity optimization
Fuente seminal	Leicht, E. A., & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.
Alias	directed community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning
Relacionados	5	6
Resumen	Directed modularity analysis extends the classic Newman-Girvan modularity framework to directed graphs, where edges carry a source and a destination. Formalized by Leicht and Newman in 2008, it partitions nodes into communities by maximizing a modularity score that accounts for each node's separate in-degree and out-degree in the null model, making it the standard approach for community detection in citation networks, information flows, and other asymmetric relational data.	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.