Methoden vergelijken

	Modulariteitsanalyse voor gerichte grafen	Gerichte gemeenschapsdetectie
Vakgebied	Netwerkanalyse	Netwerkanalyse
Familie	Machine learning	Machine learning
Jaar van ontstaan	2008	2008
Grondlegger	Leicht, E. A. & Newman, M. E. J.	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.
Type	Community detection / graph partitioning	Graph partitioning / modularity optimization
Oorspronkelijke bron	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.
Aliassen	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
Verwant	5	6
Samenvatting	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.