Bandingkan metode

	Analisis Modularitas Terarah	Analisis Modularitas
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2008	2004
Pencetus	Leicht, E. A. & Newman, M. E. J.	Newman, M. E. J. & Girvan, M.
Tipe	Community detection / graph partitioning	Community detection / graph partitioning
Sumber perintis	Leicht, E. A., & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Alias	directed community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Terkait	5	5
Ringkasan	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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.