قارن الطرق

	اكتشاف المجتمعات الموجهة	مركزية البينية الموجهة
المجال	تحليل الشبكات	تحليل الشبكات
العائلة	Machine learning	Machine learning
سنة النشأة	2008	1977
صاحب الطريقة	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.	Freeman, L. C.
النوع	Graph partitioning / modularity optimization	Centrality measure (directed graph)
المصدر التأسيسي	Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.
الأسماء البديلة	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning	directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness
ذات صلة	6	5
الملخص	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 Betweenness Centrality extends Freeman's classic betweenness measure to directed graphs, quantifying how often a node lies on the shortest directed paths between all other pairs of nodes. It identifies gatekeepers, brokers, and bottlenecks in asymmetric flows such as information cascades, citation networks, and organizational hierarchies.