השוואת שיטות

	זיהוי קהילות מכוונות	מרכזיות בין-דרגית מכוונת
תחום	ניתוח רשתות	ניתוח רשתות
משפחה	Machine learning	Machine learning
שנת המקור	2008	1977
הוגה השיטה	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.	Freeman, L. C.
סוג	Graph partitioning / modularity optimization	Centrality measure (directed graph)
מקור מכונן	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.
כינויים	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning	directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness
קשורות	6	5
תקציר	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.