Bandingkan metode

	Deteksi Komunitas Terarah	Sentralitas Antara Terarah
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2008	1977
Pencetus	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.	Freeman, L. C.
Tipe	Graph partitioning / modularity optimization	Centrality measure (directed graph)
Sumber perintis	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.
Alias	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning	directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness
Terkait	6	5
Ringkasan	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.